I'm a professor at U Michigan and lead a course on climate change problem solving. These articles often come from and contribute to the course.
By: Dr. Ricky Rood , 5:47 AM GMT on February 10, 2012
Using Predictions to Plan: Case Study – La Nina and the Missouri River (2)
Earlier articles in this series:
Extreme Weather: Can we use predictions to plan?
La Nina and the Missouri River (1)
Link to NCPP to Missouri River Basin Pilot
The purpose of this series of articles is to explore how we might use model predictions and projections to plan better for extreme events. It is a mix of seasonal climate prediction and decadal-to-centennial climate projections. What I want to do is to translate information from observational studies and model predictions and make that information usable by someone. From my teaching of climate-change problem solving, I have concluded that it is this translation of information that is the most essential missing ingredient in the usability of climate knowledge. There is a LOT of information and knowledge, but it is not easy to use. An interest of mine is to develop templates on how to use that knowledge – and of course, by doing so in these blogs to provide some transparency into the use of climate information.
The previous entry made a start on the problem, but as in many starts it was naïve. It did provide a sanity check that tells us that there is documented variability of precipitation in the Missouri River basin, correlated with La Nina. But, at first blush, the La Nina variability in this region is towards drier conditions. We also know that what determines a flood is far more complex than “it rains a lot.” So that start motivates me to step back and think about all of the pieces – or mechanisms – that might work in concert to produce a flood. I will start with a map and a few pictures.
Figure 1 is a map of the Missouri River Basin. The headwaters of the Missouri River are in the Rocky Mountains in a span from central Colorado to Montana. For the upper Missouri River, the ranges in Wyoming and Montana are the most important.
Figure 1: Map of the Missouri River Basin
I have marked up this figure a bit in Figure 2. I put in some triangles to represent the mountains. Based on the paper I discussed in the first entry, that naïve start, Item 1 points to the region where there is a late spring and early summer deficit of rain associated with La Nina. Up in the mountains of Montana I have marked Item 2, that La Nina is associated with more snow in the winter.
Figure 2: Missouri River Basin with mountains symbolically marked by little hats along with the locality of precipitation variability that is linked to the La Nina cycles.
So I want to do two things here. First, where did I get that information about La Nina and snow in Montana? The Climate Prediction Center keeps a remarkable amount of information. That’s the good news. The bad news is that it is not always easy to find the information, and when you do, sometimes it needs translation. Here is their page ENSO Temperature and Precipitation Composites. Figure 3 contains my markups of a couple of figures for the composite anomalies and the composite frequency.
Figure 3: From the Climate Prediction Center. These are composite pictures, meaning that a set of La Nina years are averaged together to show what La Nina looks like. The figure plots anomaly which is the difference from an average calculated for the years 1981-2010. Hence, the composite is the average difference of a La Nina year from the average of all of the years in 1981-2010. The frequency is what percentage of the years do you see this pattern of average differences. These are for January, February, and March.
If you compare carefully with the maps in Figures 1 or 2, especially in northwestern Wyoming, La Nina suggests larger amounts of snow. The frequency map says that this pattern of difference occurs about 80% of the time. There are also positive snow cover anomalies in northwestern Colorado, but the rivers here, flow into the Missouri relatively far downstream. The strong positive snow cover anomaly in the mountains of Idaho are not in the Missouri River Basin.
The second point that I want to emphasize here is the emergence of the fact that flood in a large river basin, like the Missouri, is related strongly to the accumulation of water in basin. Therefore, variables like snow cover and soil moisture are more directly important to evaluating flood risk than, say, instantaneous rain amounts. This has consequences for the type of information that is needed from climate models. More information is needed from climate models than temperature and precipitation. We need estimates of, in this case, the storage of water in the environment. It also points out that what happens in one region in an earlier season is an important part of the information that is needed; that is, we need to determine connections.
My goal in this series is to try to write down the process and a template to make it easier for me to think about this problem the “next time.” So what do I have so far – and this will be subject to revision
Plausibility: Do I have a plausible, observational or experiential, foundation to expect a relationship between a mode of variability (here, La Nina) and an impact (here, Upper Missouri River Flood)?
Geography: What happens to a place is strongly influenced by the geography. What are the characteristics of the geography that influence behavior? In this case, for example, mountains influence the storage of water that ultimately ends up in the Missouri River.
Knowledge: We need to identify the type of knowledge that is needed, and location of sources of that knowledge. We need to know if there are existing, trusted sources that synthesize existing knowledge. We need to know if we can find pieces of usable knowledge in from trusted sources. We need to know if we need to generate knowledge to fill in the gaps to complete the knowledge base.
Connections: What pieces are connected together?
I will complete and refine this in future entries in the series.
Link to NCPP to Missouri River Basin Pilot
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