Develop high-resolution research data for climate change studies and applications

A grand challenge for Earth System science is to translate the influence of global processes that affect our climate into specific regional and local impacts. The research in CISL combines knowledge of Earth System models, downscaling methods, scientific workflows for large data sets, statistics, and the needs and constraints of local stakeholders. This effort therefore integrates CISL expertise in data science and impact assessment with the goal of transferring climate science into useful products for decision making in adaptation research and risk analysis.

In collaboration with a broad range of public and private laboratories and universities, the mission of the Regional Integrated Science Collective (RISC) is to generate high-quality regional-scale scenarios of projected climate change, make them widely available to the broader research community, and develop tools and methods for analyzing impacts, vulnerability, and adaptation options. RISC’s placement in CISL shows the close ties between evaluating climate models and quantifying uncertainty using statistics. RISC also reaches out to the broader decision-making and policy communities by integrating mathematical analyses into a more immediate and pragmatic realm. RISC has responsibility for serving large and multifaceted numerical experiments, so it is well aligned with CISL’s mission of data support to the climate science community.

Uncertainty analysis
Observations (Livneh) and simulations (current, 1971–1999 and future, 2071–2099) of near-surface temperature (MJJA) for two different regional climate models (RegCM and WRF) driven by two different global models (GFDL and MPI) at 25-km resolution. Analyzing and comparing different types of uncertainty is important for more usefully and completely representing uncertainty in future climate.


RISC also develops and maintains the Weather and Climate Impacts Assessment Science Program (WCIASP) to investigate uncertainty in climate change research, study extreme weather and climate events, and support climate and health impacts workshops.

High-resolution Research Data for Climate Change Studies

The Regional Integrated Science Collective (RISC) collaborates with public and private laboratories and universities to generate and evaluate regional-scale climate change scenarios using high-resolution regional climate models. These community collaborations:

  • Combine global and regional climate models to project regional climate change over North America and analyze the resultant uncertainty, particularly focusing on process-level analysis.

  • Produce high resolution, 150-year regional climate model simulations for North America.

The strategic value of this activity is seen in its support of CISL’s second strategic goal to “Enhance the effective use of current and future computational systems by improving mathematical and computational methods for Earth System models and related observations,” and in particular, Imperative 2.1 to “Advance data-centric research, specifically: develop and apply novel data science techniques for regional climate change studies using high-resolution research data sets.”

The research in RISC combines knowledge of Earth System models, downscaling methods, statistical techniques and scientific workflows for large data sets, and the needs and constraints of local stakeholders. This effort therefore integrates CISL expertise in data science and impact assessment with the goal of transferring climate science into useful products for decision making in adaptation research and risk analysis. Furthermore, guided by the NCAR strategic plan, CISL research improves predictions of weather and climate and estimations of their impacts. CISL tackles the challenges of quantifying the uncertainty in predictions in ways that are useful for decision making and policy.

Further development of NA-CORDEX, additional simulations, and preliminary analysis. The Co-ordinated Regional Climate Downscaling Experiment (CORDEX) has been ongoing for several years, and it may be seen as an expansion of the North American Regional Climate Change Assessment Program (NARCCAP) in the context of an international program. Regional models are driven by the more recent CMIP5 GCMS, for a 150-year time period (through 2100) and the more recently developed representative concentration pathways (RCP) are used, including RCP8.5 and 4.5. The goal is to combine global and regional climate models to project regional climate change over North America and analyze resultant uncertainty, particularly focusing on process-level analysis.

North America CORDEX has been slow to advance through production of simulations due to lack of sufficient funding from U.S. funding agencies, yet simulations continue to be performed. Linda Mearns and William Gutowski of Iowa State are co-chairs of NA-CORDEX. In collaboration with Iowa State University and the University of Arizona, simulations are being performed for a 150-year time period (1950-2100) over most of North America, approximately the same domain as that of NARCCAP, with two different regional climate models (RegCM4 and WRF), at two different spatial resolutions (50- and 25-km) using ERA-Interim boundary conditions and boundary conditions from three different GCMs that span the equilibrium climate sensitivity of the CMIP5 collection of global climate model simulations. An earlier version of RegCM (RegCM2) was NCAR’s original regional climate model.

Specifically, in RISC, in addition to the RegCM simulations produced last year at 25 and 50 km using the Max Planck Institute (MPI) global model boundary conditions for the RCP 8.5 concentration pathway, M. Bukovsky has produced similar simulations using WRF driven by the GFDL-ESM2M global model. Simulations with WRF driven by the HadGEM GCM have been reproduced. The completion of these runs allowed us to finish producing a 2 x 2 x 3 matrix of simulations. The figure above presents the temperature results of two different regional climate models driven by two different GCMs for the Central Plains area of the U.S. Note that the intensity of change in temperature (future vs. current) is more dictated by the Global Climate Model (GCM) than by the Regional Climate Model (RCM). Other simulations have been produced by other groups, most notably in Canada using the Canadian regional climate model (CRCM5). The NA-CORDEX website (na-cordex.org) lists all simulations being performed.

Production of 12-km simulations for the new DOE project (FACETS) with WRF. Expanding on the NA-CORDEX work, M. Bukovsky has produced additional simulations with WRF at 12-km resolution for two time slices, one for the current and one for the future (end of century) using three different GCMs as drivers over North America. These are being matched with simulations using RegCM being performed at Iowa State. Analysis of these runs is underway.

Further archiving of NA-CORDEX data. Seth McGinnis and student assistant Daniel Korytina have made rapid progress in preparing and archiving subsets of NA-CORDEX data for all simulations completed so far. This includes archiving temperature and precipitation data at multiple time scales (sub-daily to seasonal) for the entire NA-CORDEX domain. All data have been published or will be shortly.

Further development of simulation data bias correction and application to NA-CORDEX data. Seth McGinnis has developed a novel technique for distribution mapping called Kernel Density Distribution Mapping (KDDM). KDDM makes use of well-established statistical methods to perform distribution mapping using non-parametric estimates of the probability distributions underlying the data sets to be bias-corrected. This technique has been evaluated against existing techniques by use of an oracle analysis, wherein each technique is used to bias-correct synthetic data and the result is compared to a perfect correction, or “oracle.” KDDM performs very well according to multiple metrics, and has the best performance on non-idealized data. It is also fast, robust, flexible, and conceptually straightforward. In FY2017, McGinnis has applied KDDM to much of the NA-CORDEX data.

RISC’s NA-CORDEX efforts are primarily supported by NSF Core funding with additional funding from the U.S. Department of Defense’s Environmental Security Technology Certification Program (ESTCP).

Metric and process-level analysis of climate model results and determination of projection credibility

Using RISC-generated data for RISC scientific projects leads to greater understanding of the strengths and limitations of the data, which is then communicated to its users. As part of this, determining the credibility of the climate projections is crucial for properly accounting for uncertainties in the projections. This aspect of RISC research has two primary goals:

  • Develop and apply novel data science techniques for regional climate change studies using high-resolution research data sets.

  • Develop statistical methods to interpret geophysical data and improve model experiments.

Analysis of snow water equivalent (SWE) in GCM-driven NARCCAP current and future simulations. Rachel McCrary and Linda Mearns have produced detailed analyses of the simulations by six different regional climate models, determining how SWE will change in the future. More information about applying the results of this work appears below. Reference: McCrary, R. and L.O. Mearns, 2018: Changes in SWE in GCM-driven regional climate models over North America (in preparation for J. Climate).

Application of metrics developed for the FACETS DOE project to the NA-CORDEX data set. A number of different metrics are being used in the FACETS project, and these are initially applied to the 10 primary locations used for metrics development. These metrics cover the commonly used measures of distributional character in temperature and precipitation and reflect the three of six ENSEMBLES metrics that are spatially local and temporally stationary. The table below presents summary results for analysis of time series of precipitation for four of the stations comparing GCM-driven WRF and RegCM4 results.

Location Season WRF RegCM4 Other
Pittsburgh, Pennsylvania Warm Exaggerated precipitation intensity Looks reasonable Overall cycle more similar by RCM than GCM
Cool Strong negative frequency bias Mild positive frequency bias WRF-MPI run: compensating error
Birmingham, Alabama Warm Negative frequency bias, correctable (thermodynamic) errors Looks reasonable Opposite seasonality in frequency bias
Cool Looks reasonable Positive frequency bias, compensating error WRF-MPI run: drier, cooler
Phoenix, Arizona Monsoon Correct timing, too intense Correct timing, rains too much year-round Cool season: RegCM4 much wetter than WRF
ARM site, Oklahoma Cool Looks reasonable Too much drizzle, DTR low by ~4°C Sign difference in frequency correlation by RCM
Fall   Missing rare high-intensity events


RISC activities in this area are supported by NSF Core funding, the U.S. Strategic Environmental Research and Development Program (SERDP) via the Department of Defense’s environmental science and technology program, and the Department of Energy project named Framework for Assessing Climate’s Energy-Water-Land Nexus using Targeted Simulations (FACETS).

Development, provision, and use of climate information including uncertainty measures with a focus on extremes for adaptation research

RISC scientists and collaborators at partner universities perform complex interdisciplinary research to support stakeholders on projects relevant to societal adaptation issues. This is valuable research in and of itself, and it also enriches the scientists’ understanding of stakeholder needs in the context of decision making under uncertainty, which then feeds into research on appropriately quantifying uncertainty in regional climate projections. The goals of this research are to:

  • Develop and apply novel data science techniques for regional climate change studies using high-resolution research data sets.

  • Develop statistical methods to interpret geophysical data and improve model experiments.

  • Analyze models’ ability to reproduce precipitation extremes that are particularly relevant for infrastructure projects and water resources.

The outcome of this effort will integrate CISL expertise in data science and impact assessment with the goal of transferring climate science into useful products for decision making in adaptation research and risk analysis.

Characterization of the change in extremes and their uncertainty for engineering designs. M. Bukovsky and A. Schroeder have begun analyzing precipitation from the NA-CORDEX simulations. This includes an examination of mean precipitation bias and change in the region surrounding Pittsburgh for a basic understanding of model performance. A comparison of 24-hour annual maximum precipitation from the raw and bias-corrected simulations has also been started for the region. This analysis is being done to understand simulation performance in producing precipitation in this region. Work is ongoing in this area to further assess the credibility of the simulations for use in this region.

Analysis of precipitation and flood extremes in the Ohio River Valley and Missouri River Basin. In conjunction with our SERDP project, we are analyzing precipitation extremes in the Ohio River Valley starting with the NARCCAP simulations. We started with annual maximum precipitation, but we will add other metrics. Further, some of the RCMs have biases in simulating extremes that are known to the team (some tend to have extremes that are consistently too light or too heavy), we have also examined bias-corrected simulations to see how the projections change when bias correction is applied (bias correction was completed using the KDDM technique). The SERDP project is developing climate-informed estimation of hydrologic extremes for robust adaptation to non-stationary climate.

In the Missouri River Basin, large-scale atmospheric moisture flow and surface snowpack conditions both modulate the risk of extreme flood events. In spring, when large-precipitation events occur at the same time as rapid snowmelt events, the likelihood of extreme runoff is high. Our goal is to establish how well the climate models represent the processes that contribute to flooding in this region. Thus far our work has focused on evaluating snow over the basin. We use snow water equivalent (SWE), which is the liquid equivalent of water stored in the snow on the ground, to study snow in models. When averaged over the region, we see that all models show a loss of SWE over the Missouri Basin during all months of the year. The biggest losses are found in the models that are driven by the HadCM3 global model. The two simulations performed with WRFG show very little snow loss, but these models have little snow on the ground to begin with.

This aspect of RISC research is supported by NSF Infrastructure Management and Extreme Events (IMEE) funding and the U.S. Strategic Environmental Research and Development Program (SERDP) via the Department of Defense’s environmental science and technology program.

Comparison of Different Downscaling Methods

A primary goal of the DOE FACETS project is to compare different downscaling methods. To this end we are producing Empirical Statistical Downscaling using the Statistical DownScaling Model (SDSM), a decision support tool for assessing local climate change impacts using a robust statistical downscaling technique. During FY2017, R. McCrary has calibrated SDSM to downscale daily total precipitation and daily minimum and maximum surface temperatures for 7 of the 10 stations being used initially in the DOE project: Pittsburg, Birmingham, Seattle, Boulder, Sioux City, SGP Arm Site, and Ft. Logan. We use odd-numbered years to “train” the SDSM model, and we test the model for overfitting and other problems using the even years. These results will be compared with the dynamical downscaling results described above in RISC’s NA-CORDEX research.

This research is supported by the Department of Energy FACETS project.

Weather and Climate Impacts Assessment Science Program (WCIASP)

RISC continued maintaining and developing the WCIASP, whose goal is to improve society’s ability to manage weather and climate risks by creating and providing research tools and methods at the critical frontiers of impact assessment science. WCIASP has three primary thrusts: investigating uncertainty in climate change research, studying extreme weather and climate events and their impacts, and supporting the Climate and Health Workshop series. In FY2017, WCIASP funded projects throughout NCAR, particularly in RAL, MMM, and in CISL/IMAGe.

This work supports CISL’s strategic imperative to advance data-centric research by combining climate data products and impact projections with companion measures of uncertainty. It also supports CISL’s imperative to foster research collaborations by drawing scientific, mathematical, and computer science researchers to NCAR and engaging them in CISL’s research efforts.

Projects funded in FY2017 included four in RAL, two in MMM, and two in IMAGe/RISC. The following products resulted from this work during the reporting period:

  • Development and maintenance of the Extremes Toolkit
  • Uncertainty in vulnerability assessments
  • Integrated uncertainty in water resource assessments
  • Climate and Health Workshop support for summer 2017
  • Communicating uncertainty and risk
  • Understanding regional climate impacts and extremes through weather typing
  • NA-CORDEX regional climate model simulations
  • Analysis of snow in the GCM-driven NARCCAP models


WCIASP activities are supported by NSF Core funding.