Seth McGinnis

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...

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.

Modeling Streamflow and Water Quality Sensitivity to Climate Change and Urban Development in 20 U.S. Watersheds

Watershed modeling in 20 large, United States (U.S.) watersheds addresses gaps in our knowledge of streamflow, nutrient (nitrogen and phosphorus), and sediment loading sensitivity to mid-21st Century climate change and urban/residential development scenarios. Use of a consistent methodology facilitates regional scale comparisons across the study watersheds. Simulations use the Soil and Water Assessment Tool. Climate change scenarios are from the North American Regional Climate Change Assessment Program dynamically downscaled climate model output. Urban and residential development scenarios are from U.S. Environmental Protection Agencys Integrated Climate and Land Use Scenarios project. Simulations provide a plausible set of streamflow and water quality responses to mid-21st Century climate change across the U.S. Simulated changes show a general pattern of decreasing streamflow volume in the central Rockies and Southwest, and increases on the East Coast and Northern Plains. Changes in pollutant loads follow a similar pattern but with increased variability. Ensemble mean results suggest that by the mid-21st Century, statistically significant changes in streamflow and total suspended solids loads (relative to baseline conditions) are possible in roughly 30-40% of study watersheds. These proportions increase to around 60% for total phosphorus and total nitrogen loads. Projected urban/residential development, and watershed responses to development, are small at the large spatial scale of modeling in this study.

Evaluation of the Surface Climatology over the Conterminous United States in the North American Regional Climate Change Assessment Program Hindcast Experiment Using a Regional Climate Model Evaluation System

AbstractSurface air temperature, precipitation, and insolation over the conterminous United States region from the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) hindcast study are evaluated using the Jet Propulsion Laboratory (JPL) Regional Climate Model Evaluation System (RCMES). All RCMs reasonably simulate the observed climatology of these variables. RCM skill varies more widely for the magnitude of spatial variability than the pattern. The multimodel ensemble is among the best performers for all these variables. Systematic biases occur across these RCMs for the annual means, with warm biases over the Great Plains (GP) and cold biases in the Atlantic and the Gulf of Mexico (GM) coastal regions. Wet biases in the Pacific Northwest and dry biases in the GM/southern Great Plains also occur in most RCMs. All RCMs suffer problems in simulating summer rainfall in the Arizona–New Mexico region. RCMs generally overestimate surface insolation, especially in the...

A new distribution mapping technique for climate model bias correction

We evaluate the performance of different distribution mapping techniques for bias correction of climate model output by operating on synthetic data and comparing the results to an “oracle” correction based on perfect knowledge of the generating distributions. We find results consistent across six different metrics of performance. Techniques based on fitting a distribution perform best on data from normal and gamma distributions, but are at a significant disadvantage when the data does not come from a known parametric distribution. The technique with the best overall performance is a novel nonparametric technique, kernel density distribution mapping (KDDM).

Surface Temperature Probability Distributions in the NARCCAP Hindcast Experiment: Evaluation Methodology, Metrics, and Results

AbstractMethodology is developed and applied to evaluate the characteristics of daily surface temperature distributions in a six-member regional climate model (RCM) hindcast experiment conducted as part of the North American Regional Climate Change Assessment Program (NARCCAP). A surface temperature dataset combining gridded station observations and reanalysis is employed as the primary reference. Temperature biases are documented across the distribution, focusing on the median and tails. Temperature variance is generally higher in the RCMs than reference, while skewness is reasonably simulated in winter over the entire domain and over the western United States and Canada in summer. Substantial differences in skewness exist over the southern and eastern portions of the domain in summer. Four examples with observed long-tailed probability distribution functions (PDFs) are selected for model comparison. Long cold tails in the winter are simulated with high fidelity for Seattle, Washington, and Chicago, Illi...

Using joint probability distribution functions to evaluate simulations of precipitation, cloud fraction and insolation in the North America Regional Climate Change Assessment Program (NARCCAP)

This study evaluates model fidelity in simulating relationships between seasonally averaged precipitation, cloud fraction and surface insolation from the North American Regional Climate Change Assessment Project (NARCCAP) hindcast using observational data from ground stations and satellites. Model fidelity is measured in terms of the temporal correlation coefficients between these three variables and the similarity between the observed and simulated joint probability distribution functions (JPDFs) in 14 subregions over the conterminous United States. Observations exhibit strong negative correlations between precipitation/cloud fraction and surface insolation for all seasons, whereas the relationship between precipitation and cloud fraction varies according to regions and seasons. The skill in capturing these observed relationships varies widely among the NARCCAP regional climate models, especially in the Midwest and Southeast coast regions where observations show weak (or even negative) correlations between precipitation and cloud fraction in winter due to frequent non-precipitating stratiform clouds. Quantitative comparison of univariate and JPDFs indicates that model performance varies markedly between regions as well as seasons. This study also shows that comparison of JPDFs is useful for summarizing the performance of and highlighting problems with some models in simulating cloud fraction and surface insolation. Our quantitative metric may be useful in improving climate models by highlighting shortcomings in the formulations related with the physical processes involved in precipitation, clouds and radiation or other multivariate processes in the climate system.

Evaluation of Snow Water Equivalent in NARCCAP Simulations, Including Measures of Observational Uncertainty

AbstractThis study evaluates snow water equivalent (SWE) over North America in the reanalysis-driven NARCCAP regional climate model (RCM) experiments. Examination of SWE in these runs allows for the identification of bias due to RCM configuration, separate from inherited GCM bias. SWE from the models is compared to SWE from a new ensemble observational product to evaluate the RCMs’ ability to capture the magnitude, spatial distribution, duration, and timing of the snow season. This new dataset includes data from 14 different sources in five different types. Consideration of the associated uncertainty in observed SWE strongly influences the appearance of bias in RCM-generated SWE. Of the six NARCCAP RCMs, the version of MM5 run by Iowa State University (MM5I) is found to best represent SWE despite its use of the Noah land surface model. CRCM overestimates SWE because of cold temperature biases and surface temperature parameterization options, while RegCM3 (RCM3) does so because of excessive precipitation. ...

Big Data Challenges and Hazards Modeling

In this work we present an overview of the challenges presented by remote sensing and other big data sources for hazards modeling and response in the world today. Big data not only provides vital information for rapid and efficient assessment of the effects and impacts of natural and anthropogenic effects, but is also an important boundary object facilitating communication and interaction between the relevant scientific, business, and governmental organizations. To effectively serve that role, big data must be credible, salient, and legitimate. The characteristics of big data are examined and we conclude that the most important ones for this application are volume, velocity, variety, and value. We present two different applications from the fields of climate and the solid earth science that are designed to solve these challenges for big data science.

Assessing NARCCAP climate model effects using spatial confidence regions

We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP) climate models using varying classes of linear regression models. Specifically, we consider how the average temperature effect differs for the various global and regional climate model combinations, including assessment of possible interaction between the effects of global and regional climate models. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We also show conclusively that results from pointwise inference are misleading, and that accounting for multiple comparisons is important for making proper inference.