ModelIng & Analysis

Our understanding of current and projected climate impacts results from our NOAA RISA team’s efforts to advance the state of the science in modeling and analysis. CIRC has also developed an advanced method for applying global climate model data to local levels, used distributed computing to hone the use of a regional climate model, created a simplified approach for understanding future changes in watersheds, and created a modeling framework to determine the cumulative impact of multiple coastal hazards. Let’s start with the better-known computer models that we employ in our research, global climate models (GCMs).

Global Climate Models (GCMs)

As their name suggests Global Climate Models (GCMs) model the whole Earth. They do this by dividing the globe into large three-dimensional, box-like cells that include the atmosphere to the space just above the ground. Inside these cells atmospheric dynamics are simulated within the computer. As with a video game or other computer simulation, the cells have a resolution that’s dependent on how much information can be packed into a given cell. For GCMs, the more cells you have to divide the world into, the more detailed and higher resolution that simulated world will be. (Incidentally, this isn’t just a metaphor; “resolution” is the term of art in the climate-modeling world.)

Yet, while engineers have consistently built higher and higher resolution into GCMs—thank you, Moore’s law—this resolution still isn’t fine enough to assess regional climate impacts in any great detail. Consider an important Pacific Northwest feature: the Cascade Mountains. Under the coarse resolutions of many GCMs, the Cascades are replaced by a kind of featureless plain that gently tilts upward toward Wyoming. This lack of detail produces a problem for climate researchers. The Cascades create a major rain shadow that makes the eastern half of the Pacific Northwest significantly drier than its western counterpart. That rain shadow needs to be accounted for. Detail needs to be put back on the map. Luckily there are two solutions: regional climate models and a process called downscaling. (See section below.) 

Regional Climate Models (RCMs)

Regional climate models (RCMs), as their name suggests, simulate climate for specific regions of the planet. Because they cover a smaller area than the entire planet, they can provide information at higher spatial resolutions (i.e., finer detail) than global climate models can using the same amount of computational resources. Finer detail alone, however, does not necessarily translate to more certainty in local projections of climate change. In fact, the opposite tends to be true: More “noise” in the form of natural climate variability frequently accompanies more detail. From basic statistics we learn that the most straightforward way to find a robust signal in noisy data is to increase our sample size. In data collection, we can increase our sample size by running an experiment many times. In modeling, we do this by running multiple simulations. Unfortunately, this kind of computing power is often beyond the resources of many research teams. Enter distributed computing. 

With participation from the Oregon Climate Change Research Institute (OCCRI), CIRC researchers Philip Mote, David Rupp, and Sihan Li completed a unique RCM project that involved simulating the regional climate of the Western United States with the help of thousands of volunteers who ran the team’s climate simulations on their personal computers. The project was called weather@home, employed the Hadley Centre’s regional climate model HadRM3P, and was completed through the distributed computing effort climateprediction.net. This allowed the researchers to create what’s called a superensemble, which is essentially multiple model runs employing different initial conditions (the precise state of the atmosphere and land surface at the start of a simulation) and perturbations of the RCM’s physics, creating what was, in effect, thousands of experiments.

Downscaling and MACA

Downscaling takes the coarse (low-resolution) data from GCMs and turns it into high-resolution data that accounts for local landscape features and local climate. The type of downscaling CIRC researchers tend to employ is called statistical downscaling. Statistical downscaling works by, in effect, “training” (another term of art) GCM data to statistically fit with local historical climate data. Getting our downscaling right accounts for a big part of what we do at CIRC. As part of our modeling and analysis effort, CIRC researcher John Abatzoglou developed and honed a downscaling method called the Multivariate Adaptive Constructed Analogs, or MACA, method. 

Learn more 

 

Global Climate Model Evaluation

The GCMs we use in our statistical downscaling are not created in-house. Instead, they come out of the fifth phase of the Coupled Model Intercomparison Project (CMIP5), an international effort to provide consistent output from the world’s GCMs. CMIP5 acts as a kind of giant library for the many GCMs, their simulations, and resulting data of the Earth’s climate made available by the dozens of modeling groups around the world.

As part of CIRC’s Integrated Scenarios project, our NOAA RISA team members, led by CIRC researcher David Rupp, performed an evaluation of data output from CMIP5 GCMs. The goal was to determine which models produced outputs that were the best statistical fit for the Pacific Northwest. This was done by testing how well the models were able to simulate various measures of the region’s historical climate. The idea was that models that faithfully reproduced the Pacific Northwest’s past climate would also produce the highest fidelity simulations of the region’s future climate. 

 

Other Computer Models

With downscaled data in hand, we feed those data into hydrologic and vegetation computer models. These computer simulations provide us the ability to simulate local climate impacts (e.g., determining the likelihood of wildfires or flooding in a region given the trends in warming and precipitation gleaned from the GCMs). CIRC researchers use a similar, though substantially different, set of computer models to simulate future changes along our coast. (For more info, see Coastal Hazards & Extremes.)

While our work with GCMs entails simulating climate to the year 2100, CIRC researchers also focus on what’s happening to our climate now. Several of our Climate Tools—including The Northwest Climate Toolbox—employ near real-time simulations of the climate that give our region’s farmers and other important decision makers essential information about drought and other impacts that happen in the here and now.

 

Sources:

Li, Sihan, Philip W. MoteDavid E. Rupp, Dean Vickers, Roberto Mera, and Myles Allen. "Evaluation of a Regional Climate Modeling Effort for the Western United States Using a Superensemble from weather@ home." Journal of Climate 28, no. 19 (2015): 7470-7488. https://doi.org/10.1175/JCLI-D-14-00808.1.

Rupp, David E., John T. AbatzoglouKatherine C. Hegewisch, and Philip W. Mote. "Evaluation of CMIP5 20th Century Climate Simulations for the Pacific Northwest USA." Journal of Geophysical Research: Atmospheres 118, no. 19 (2013). https://doi.org/10.1002/jgrd.50843.

 

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