North American Regional Climate Change Assessment Program Research Articles

Estimated changes in wind speed and wind power density over the western High Plains, 1971–2000

This manuscript presents the results of research on the temporal patterns in wind speed and wind power density from 1971 to 2000. The study area is across the western High Plains states east of the Rocky Mountains in an area which has a proven wind power resource. Policies and economic analyses involving the rapidly expanding wind power industry have often assumed a constant in the wind resource; however, any temporal pattern or trend in wind speeds can have a meaningful impact on the reliability of wind power as an energy resource. Using data provided by the North American Regional Climate Change Assessment Program (NARCCAP) to analyze decadal and seasonal trends of wind data, this study shows that from 1971 to 2000 there were some notable changes in the NARCCAP simulated wind velocities over the study region. Wind speed trends across the central High Plains of the USA were most notable across the western portion of the study area along the higher terrain near the front range of the Rocky Mountains. The most significant changes occurred during winter and spring when a large portion of the study area experienced the most substantial decrease in wind speed, with a 20% reduction in wind power density during spring across the western portion of the study area. During summer and fall, the trends are less noticeable, with only very small changes in the summer. Fall was the only season that saw widespread increased values of wind power density from the 1970s to 1990s, with increases of nearly 10% in some southern areas of the study area. Based upon the analysis of the data and previous literature, it is theorized that these changes could be the result of changing synoptic patterns across the study region.

Urban precipitation extremes: How reliable are regional climate models?

We evaluate the ability of regional climate models (RCMs) that participated in the North American Regional Climate Change Assessment Program (NARCCAP) to reproduce the historical season of occurrence, mean, and variability of 3 and 24‐hour precipitation extremes for 100 urban areas across the United States. We show that RCMs with both reanalysis and global climate model (GCM) boundary conditions behave similarly and underestimate 3‐hour precipitation maxima across almost the entire U.S. RCMs with both boundary conditions broadly capture the season of occurrence of precipitation maxima except in the interior of the western U.S. and the southeastern U.S. On the other hand, the RCMs do much better in identifying the season of 24‐hour precipitation maxima. For mean annual precipitation maxima, regardless of the boundary condition, RCMs consistently show high (low) bias for locations in the western (eastern) U.S. Our results indicate that RCM‐simulated 3‐hour precipitation maxima at 100‐year return period could be considered acceptable for stormwater infrastructure design at less than 12% of the 100 urban areas (regardless of boundary conditions). RCM performance for 24‐hour precipitation maxima was slightly better, with performance acceptable for stormwater infrastructure design judged adequate at about 25% of the urban areas.

Evaluation of present and future North American Regional Climate Change Assessment Program (NARCCAP) regional climate simulations over the southeast United States

In order to make well‐informed decisions in response to future climate change, officials and the public require reliable climate projections at the scale of tens of kilometers, rather than the hundreds of kilometers that the current atmosphere–ocean general circulation models provide. Recent efforts such as the North American Regional Climate Change Assessment Program (NARCCAP) aim to address this need. This study has two principal aims: (1) evaluate the seasonal performance of the NARCCAP simulations over the southeast United States for both present (1971–2000) and future (2041–2070) periods and (2) assess the impact of a performance‐based weighting scheme on bias and uncertainty. Application of the weighting scheme results in a substantial reduction in magnitude and percent area exhibiting significant bias in all seasons for both temperature and precipitation. The weighting scheme is then expanded to evaluate future change. Temperature changes are universally positive and outside the bounds of natural variability over the entire region and in all seasons. Application of the weighting scheme tightens confidence intervals by as much as 1.6°C. Future precipitation changes are modest, are of mixed sign, and vary by season and location. Though uncertainty is reduced by as much as 50%, the projected changes are generally not outside the bounds of natural background variability. Thus, under the NARCCAP simulations, stress on water resources is most likely to come from increased temperatures and not changes in mean seasonal precipitation. For energy use, the implication is that the ∼3°C temperature increase during the peak use summer season may place additional strain on power grids.

Dynamical random-set modeling of concentrated precipitation in North America

In order to study climate at scales where policy decisions can be made, regional climate models (RCMs) have been developed with much finer resolution ( 50 km) than the 500 km resolution of atmosphere-ocean general circulation models (AOGCMs). The North American Regional Climate Change Assessment Program (NARCCAP) is an international program that provides 50-km resolution climate output for the United States, Canada, and northern Mexico. In Phase I, there are six RCMs, from which we choose one to illustrate our methodology. The RCMs are updated every 3 hours and contain a number of variables, including temperature, precipitation, wind speed, wind direction, and air pressure; output is available from 1968–2000 and from 2038–2070. Precipitation is of particular interest to climate scientists, but it can be difficult to study because of its patchy nature: At hourly-up-to-monthly time scales, there are generally many zeroes over the precipitation field. In this research, we study sets of concentrated precipitation (i.e., the union of RCM pixels whose precipitation is above a given threshold), where we are interested in the way these sets evolve from one 3-hour period to the next. Assuming the sets are a realization of a time series of random sets, we are able to build dynamical models for the passage of rainfall fronts over 1-2 days. The dynamics are characterized by a growth/recession model for a time series of random sets, with several parameters that control how the concentrated precipitation changes over time.

A Comparison of Statistical and Dynamical Downscaling of Winter Precipitation over Complex Terrain

Abstract Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November–May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation from a 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.

Modeling the Potential Impacts of Climate Change on a Small Watershed in Labrador, Canada

Hydroelectric power producers are strongly dependent on the climate to deliver the fuel necessary to generate electricity. This fuel source is watershed runoff, which is manifested as the balance between components of the climate system, primarily precipitation and evapotranspiration. Climate change threatens to alter global hydrological regimes and impacts need to be assessed to determine production vulnerabilities and opportunities. In this paper, dynamically downscaled regional climate models from the North American Regional Climate Change Assessment Program (NARCCAP) are combined with statistical bias correction techniques to generate 30-year time series of temperature and precipitation for a base period (1980s) and a future period (2050s). These time series are transformed into streamflow using the WATFLOOD hydrological model that has been calibrated for a sub-basin of the Churchill River. Results are consistent with IPCC results and show increasing mean annual streamflow of approximately 9% between the base and future periods with larger increases in winter runoff and little or no change during late summer and fall. Inter-model comparison and probabilistic methods are used to provide further insight into simulation results.

Comparing and Blending Regional Climate Model Predictions for the American Southwest

We consider the problem of forecasting future regional climate. Our method is based on blending different members of an ensemble of regional climate model (RCM) simulations while accounting for the discrepancies between these simulations, under present day conditions, and observational records for the recent past. To this end, we develop Bayesian space-time models that assess the discrepancies between climate model simulations and observational records. Those discrepancies are then propagated into the future to obtain blended forecasts of 21st century climate. The model allows for location-dependent spatial heterogeneities, providing local comparisons between the different simulations. Additionally, we estimate the different modes of spatial variability, and use the climate model-specific coefficients of the spatial factors for comparisons. We focus on regional climate model simulations performed in the context of the North American Regional Climate Change Assessment Program (NARCCAP). We consider, in particular, simulations from RegCM3 using three different forcings: NCEP, GFDL and CGCM3. We use simulations for two time periods: current climate conditions, covering 1971 to 2000, and future climate conditions under the SRES A2 emissions scenario, covering 2041 to 2070. We investigate yearly mean summer temperature for a domain in the South West of the United States. The results indicated the RCM simulations underestimate the mean summer temperature increase for most of the domain compared to our model.

Estimates of future flow, including extremes, of the Columbia River headwaters

Streamflow projections, including extremes, for the 2050s for the Columbia River headwaters above Donald are obtained by downscaling four regional climate models of the North American Regional Climate Change Assessment Program (NARCCAP) suite and subsequent driving of a hydrologic model. We employ the entire model chain from global and regional climate models, station‐based statistical downscaling, and a fully distributed, physically based hydrologic model and verify the results against observed streamflow. The performance is model dependent but is generally encouraging enough to justify the application of the climate scenarios. A general warming of about 2°C is projected and, on average, slightly drier conditions, especially in late summer. We find evidence that the projected changes are elevation dependent and relatively small scale, with decreasing signals with higher elevations. All models project a shift of the hydrograph toward a more rain‐fed regime, with peak flows occurring in June instead of July. Annual peak flow is projected to not increase, and August low flow decreases in all four models. With nonshrinking (static) glaciers, relatively high melting rates are simulated for August and September that partly compensate for the shifted hydrograph; this enhanced glacier melt is also detected in simulated historic Columbia headwater flow. The static approximation is supported by a heuristic seasonal sensitivity analysis that suggests a moderate average areal glacier recession of about 10% for the midcentury. We discuss the need for a dynamic glacier component for a refined assessment of future drought risk.

Climate change impact on the hydrology of Spencer Creek watershed in Southern Ontario, Canada

Summary This study is for the assessment of climate change impact on the future hydrology of Spencer Creek watershed located in Southern Ontario, Canada under the A2 scenario of the Special Report on Emissions Scenarios (SRES). The study is particularly concerned with changes in the climate variables and the seasonal and interannual flow regimes of the study area. The analysis also addresses the annual exceedance probability of extreme precipitation, temperature and flow events. Potential hydrologic effects of climate change were assessed for the Spencer Creek by imposing changes in precipitation and temperature derived from the North American Regional Climate Change Assessment Program (NARCCAP) climate simulations between 2040 and 2069. The climate models results were used as input to three hydrological models to produce projections of Spencer Creek watershed discharges. The results were compared to the observed discharges between 1989 and 2008. Notwithstanding the variability between the different regional climate model and hydrological model projections that envelop the future climate scenarios and the hydrological modeling uncertainties, all future simulations show an increase in the average interannual discharge, but also a noteworthy change in the seasonal distribution of the discharges. While the former is mainly attributed to the average annual precipitation, which tends to increase, the change in seasonal distribution of discharges is in line with the temperature increase of the winter and spring seasons that results in earlier snowmelt. Important changes were found in the annual exceedance probability (recurrence interval) of the extreme precipitation, temperature and runoff events.

Evaluating climate change over the Colorado River basin using regional climate models

[1] We use regional climate model (RCM) simulations from the North American Regional Climate Change Assessment Program (NARCCAP) to evaluate implications of climate change for the discharge of the Colorado River in the mid-21st century. We compare historical RCM simulations and simulations from their host global general circulation models (GCMs) to one-eighth degree gridded observations of precipitation, surface air temperature, and runoff (generated by the Variable Infiltration Capacity (VIC) land surface model forced with gridded observations) for the historical period 1970–1999. The RCMs capture the primary features of observations better than their host GCMs in part because of their ability to better represent strong upward lift in topographically complex regions. Although the RCMs do not significantly improve the simulation of precipitation, their ability to better represent surface temperature in mountainous regions has important effects on simulations of evapotranspiration, snowpack, and runoff. In the Colorado River basin, projected mid-21st century runoff changes are mostly impacted by the combination of snow cover change in winter, temperature change in spring, and precipitation change in summer. In particular, the response of cold season temperatures in headwater streams is key to determining the basin's susceptibility to a warming climate. Because of the cooler temperature and higher snowline in RCMs relative to GCMs, the RCMs project less warming in the spring and thus generate smaller decreases in runoff, both during spring and annually, as compared with GCMs. Changes in surface air temperature, runoff, and snow water equivalent at high elevations all indicate that headwater streams of the Colorado River are less susceptible to a warming climate in climate change simulations that use RCMs than in simulations that use GCMs. Nonetheless, the 50 km NARCCAP grid resolution has some limitations in resolving orographic effects, which suggests that there may remain residual biases in the climatic sensitivity of the RCM simulations.

Future changes in intense precipitation over Canada assessed from multi‐model NARCCAP ensemble simulations

AbstractAnnual maxima (AM) series of precipitation from 15 simulations of the North American Regional Climate Change Assessment Program (NARCCAP) have been analysed for gridpoints covering Canada and the northern part of United States. The NARCCAP Regional Climate Models' simulations have been classified into the following three groups based on the driving data used at the RCMs boundaries: (1) NCEP (6 simulations); (2) GCM‐historical (5 simulations); and (3) GCM‐future (4 simulations). Historical simulations are representative of the 1968‐2000 period while future simulations cover the 2041‐2070 period. A reference common grid has been defined to ease the comparison. Multi‐model average intensities of AM precipitation of 6‐, 12‐, 24‐, 72‐, and 120‐h for 2‐, 5‐, 10‐, and 20‐year return periods have been estimated for each simulation group. Comparison of results from NCEP and GCM‐historical groups shows good overall agreement in terms of spatial distribution of AM intensities. Comparison of GCM‐future and GCM‐historical groups clearly shows widespread increases with median relative changes across all gridpoints ranging from 12 to 18% depending on durations and return periods. Fourteen Canadian climatic regions have been used to define regional projections and average regional changes in intense precipitation have been estimated for each duration and return period. Uncertainties on these regional values, resulting from inter‐model variability, were also estimated. Results suggest that inland regions (e.g. Ontario and more specifically Southern Ontario, the Prairies, Southern Quebec) will experience the largest relative increases in AM intensities while coastal regions (e.g. Atlantic Provinces and the West Coast) will experience the smallest ones. These projections are most valuable inputs for the assessment of future impact of climate change on water infrastructures and the development of more efficient adaptation strategies. Copyright © 2011 Royal Meteorological Society

Latent Variable Modeling for Integrating Output from Multiple Climate Models

Numerical models of atmosphere–ocean circulation are widely used to understand past climate and to project future climate change. Although the same laws of physics, chemistry, and fluid dynamics govern any general circulation model, each model’s formulations and parameterizations are different, yielding different projections. Notwithstanding, models within an ensemble will have varying degrees of similarity for different outputs of interest. Multi-model ensembles have been used to increase forecast skill by using simple or weighted averages where weights have been obtained by considering factors such as estimated model bias and consensus with other models (Giorgi and Mearns, J. Clim. 15:1141–1158, 2002, Geophys. Res. Lett. 30:1629–1632, 2003; Tebaldi et al., Geophys. Res. Lett. 31:L24213, 2004, J. Clim. 18:1524–1540, 2005). This paper considers an alternative view of multi-model ensembles. For use with the North American Regional Climate Change Assessment Program (NARCCAP), multivariate statistical models are employed to characterize modes of similarity within the members of an ensemble. Specifically, we propose a spatially-correlated latent variable model which facilitates the exploration of when, where, and how regional climate models are similar, and what factors best predict observed locations of model convergence.

Regional Extreme Monthly Precipitation Simulated by NARCCAP RCMs

Abstract This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10% of months during the cold half of the year (October–March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations.

Streamflow in the upper Mississippi river basin as simulated by SWAT driven by 20th Century contemporary results of global climate models and NARCCAP regional climate models

We use Soil and Water Assessment Tool (SWAT) when driven by observations and results of climate models to evaluate hydrological quantities, including streamflow, in the Upper Mississippi River Basin (UMRB) for 1981―2003 in comparison to observed streamflow. Daily meteorological conditions used as input to SWAT are taken from (1) observations at weather stations in the basin, (2) daily meteorological conditions simulated by a collection of regional climate models (RCMs) driven by reanalysis boundary conditions, and (3) daily meteorological conditions simulated by a collection of global climate models (GCMs). Regional models used are those whose data are archived by the North American Regional Climate Change Assessment Program (NARCCAP). Results show that regional models correctly simulate the seasonal cycle of precipitation, temperature, and streamflow within the basin. Regional models also capture interannual extremes represented by the flood of 1993 and the dry conditions of 2000. The ensemble means of both the GCM-driven and RCM-driven simulations by SWAT capture both the timing and amplitude of the seasonal cycle of streamflow with neither demonstrating significant superiority at the basin level.

Evaluation of precipitation in the Intermountain Region as simulated by the NARCCAP regional climate models

We evaluated the precipitation climatology of the Intermountain Region (IR) as generated by the six regional climate models of the North American Regional Climate Change Assessment Program (NARCCAP). A complex combination of the precipitation annual and semiannual cycles with their different phases form four major climate regimes over the IR. Each model produces systematic biases in the central IR where these different climate regimes meet. The simulated annual cycles are universally too strong, and the winter precipitation is too large. On the other hand, the semiannual cycles are relatively well produced. The strong annual cycles and the excess winter precipitation obscure the signals of spring/summer precipitation and may have led to false signals of the El Niño‐Southern Oscillation (ENSO) found in the central IR. Therefore, caution is advised when interpreting the simulated NARCCAP precipitation for the IR.

The Earth System Grid: Enabling Access to Multimodel Climate Simulation Data

By leveraging current technologies to manage distributed climate data in a unified virtual environment, the Earth System Grid (ESG) project is promoting data sharing between international research centers and diverse users. In transforming these data into a collaborative community resource, ESG is changing the way global climate research is conducted. Since ESG's production beginnings in 2004, its most notable accomplishment was to efficiently store and distribute climate simulation data of some 20 global coupled ocean-atmosphere models to the scores of scientific contributors to the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC); the IPCC collective scientific achievement was recognized by the award of a 2007 Nobel Peace Prize. Other international climate stakeholders such as the North American Regional Climate Change Assessment Program (NARCCAP) and the developers of the Community Climate System Model (CCSM) and of the Climate Science Computational End Station (CC...

The North American Regional Climate Change Assessment Program (NARCCAP): Overview of Phase II results

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