Training Center in Shephardstown, West Virginia. My name is Ashley Fortune, and I would like

to welcome you to our webinar series, held in partnership with the U.S. Geological Survey's

National Climate Change and Wildlife Science Center in Reston, Virginia.

The NCCWSC Climate Change, Science, and Management webinar series highlights their sponsored

science projects related to climate change impacts and adaptation, and aims to increase

awareness and inform participants like you, about potential and predicted climate change

impacts on fish and wildlife. I'd like to welcome Shawn Carter, senior scientist

at the NCCWSC, to introduce our speaker. Shawn? Shawn Carter: Thanks, Ashley. Today, I'm happy

to have Dr. Phil Mote present on some of the work that he's been doing with our Northwest

Climate Science Center. Phil is a professor in the College of Earth's

Ocean and Atmospheric Sciences, at Oregon State University. He's also the director for

OCCRI, the Oregon Climate Change Research Institute. He also is director of Oregon Climate

Services, which is the state climate office for Oregon.

Phil's current research interests include scenario development, regional climate change,

regional climate modeling, and adaptation to climate change.

Phil is also part of the leadership team at the Northwest Climate Science Center, and

has been involved with the IPCC National Climate Assessment, and also the National Research

Council. Without further ado, I'd like to turn it over

to Phil. Phil Mote: Thank you Shawn, Ashley, and Holly,

for setting this up. Thanks to all of you for tuning in. I look forward to your questions

at the end. On the first slide here you see the cast of folks involved in this work.

My colleague, Dave Rupp, here at Oregon State University, John Abatzoglou and Katherine

Hegewisch of University of Idaho. Dennis Lettenmaier, who leads the hydrology

group at University of Washington, along with Julie Vano, Homero Flores, and Matt Stumbaugh,

Dominique Bachelet and John Kim, who've done the vegetation modeling, I should also add

Dave Turner from Oregon State University. This is the culmination of a project funded

by the Northwest Climate Science Center, with support also from the NOAA regional entity

here, the Climate Impacts Research Consortium. John Abatzoglou and I also had some funding

from the USDA funded project, Regional Approaches to Climate Change and Pacific Northwest Agriculture.

I particularly want to thank Gus Bisbal, Director of the Northwest Climate Science Center for

supporting this vision of providing these scenarios to the Northwest region, which also

led to a lot of conversations with CSCs around the country.

This project is wrapping up, but we don't have all the results in yet. You'll see our,

in some cases, preliminary results. We have a workshop coming up in Portland,

Oregon, two weeks from today. We'll spend a whole day on this topic. We'll have our

final report due to the Northwest CSC three months after the completion of the project.

The motivation for this work was a recognition that natural resource managers were paying

attention to climate change science, but at a bit of a loss for how to apply it.

If someone told them, "Your habitat conservation plan or this species management plan is no

good, because you haven't considered climate change," even if they fully bought the science

of climate change, they wouldn't really know where to turn, or how to apply that to such

an activity. This is the space that Climate Science Centers

intend to occupy, providing such guidance. It was natural that the CSC would play a lead

role in this. What we were hearing was a need for a complete

and scientific description of what the future would look like in the Northwest Region.

Water availability, soil moisture and stream flow, snow cover, flood risk. How will the

drivers of climate impact the change in temperature and participation? How will vegetation change?

The objectives of this project are to use the best available science to describe the

future climate, hydrology, and vegetation. There are fully coupled regional models that

have vegetation and hydrology. But, with just a single model as we've learned

and as you'll see, a single model is only able to produce a modest spread of results,

which may not accurately represent the true range of possibilities, because of how models

are constructed and choices that are made. We wanted to be able to characterize the uncertainties

of the system. That meant using a different approach than just one single, wonderful model.

We want to use a range of climate inputs, and at least two impact models.

We have two hydrology models, one of which is nearly done with a set of simulations from

20 climate model scenarios, and a second one that will follow shortly.

And then we also have two vegetation models in play. We're fortunate to have a new generation

of global climate models that were released in the last few years, and what we've done

is coordinate the climate model outputs with the inputs to both the hydrologic and vegetation

modeling. And we have a spectrum of audiences ranging from, "I want a simple quality of

description, may be a number here and there", to researchers who want the full visible data

and the resolution of the model outputs for some additional scientific analysis or modeling.

The climate scenarios that we're using as inputs come from the Coupled Model Intercomparison

Project stage five, CMIP5. This is a coordinated global modeling effort that uses 41 models

that have been contributed to date for the 20th century.

About 25 or 30 that have done simulations for the 21st century. These started to be

available in the year 2011 and they're still being uploaded to various archives.

We wanted to evaluate these models on the regional scale, and we've done this now both

for the Northwest and to a lesser extent the Southwest, but also for the Southeast, and

then recommend some models that are top tier. There are enough models that we can get a

pretty good spread of results using the ones that performed well in the 20th century.

Now I would note that there is a supposition here that the performance of a model in the

20th century is an indication of how well it will do in the 21st century. We don't know

whether that's true, but it seems reasonable and we use model performance to shape our

choices for models in the future. Then, John Abatzoglou at the University of

Idaho who had developed this MACA, Multivariant Adaptive Constructed Analogue approach to

downscaling, has downscaled 20 GCMs for the whole continental U.S.

We were initially intending to just do the Northwest, but it turned out not to be that

much work to do the whole continental U.S. Then we also have a comparison, which I won't

talk much about. A comparison with the previous generation of models, CMIP3, which was released

around the year 2005. The drivers for these global models are what

are called Representative Concentration Pathways, or RCPs. You'll see this in a number of slides,

it's worth taking a few minutes to explain. Socioeconomic modeling has postulated a wide

variety of futures for the world depending on both market and non market forces, on policy

choices, on availability of fossil fuels and a wide range of other considerations, and

also global population. I draw your attention to the orange curve,

the RCP8.5. This is a world in which development is fairly unfettered and in which coal is

widely available and remains cheap, and the developing countries are able to rapidly follow

the developed world into a much more prosperous and consumptive future.

RCP6 is a more modest version of that, in which the carbon dioxide amounts by the end

of the century don't quite reach 700, versus over 900 for RCP8.5. RCP4.5 is more of a gentle,

sustainable future where carbon dioxide amounts level off by around 2070 at 540 parts per

million. Climate continues to change a little bit after

that, but it's a rather different future. And then finally RCP3PD, which is also known

as RCP2.6, is intended to reflect the postulated success of policies which would reduce greenhouse

gas emissions so substantially as to limit the global temperature change to two degrees

Celsius, which is a stated goal of many governments. It's all a very totally Pollyanna view of

the world, dialing back emissions so dramatically would be politically and economically a very

heavy load. For this work we're going to focus on the

gentle, sustainable, RCP4.5, the green curve and RCP8.5. Those are shown here in comparison

with the earlier generation, the CMIP3 model input and that's the dashed curve.

The RCPs also extend beyond the year 2100, that's an important distinction. You see there

the RCP2.6, which initially is fairly similar to the others, to about 2025, and then departs

rather dramatically. The Y-axis here is Radiative Forcing.

This is essentially how much extra energy is added in watts to each square meter over

the earth. The amounts are initially a little over one watt per square meter, and they rise

from anywhere from 2.6 by the year 2100, all the way up to 8.5, and that's what the numbers

for the RCP correspond to. These are the 20 models that we're using in

this study, listed in alphabetical order by country. And you can see there a number of

entries from several different countries. This is, again, a subset of the total ones

available, and the next several slides will show that the climate variables for the northwest,

including a spread across the models to show the uncertainty.

They're smoothed for easier reading. Later you'll see some un smoothed curves that indicate

why we want to smooth them. This is a result of a model ranking approach that David Rupp

came up with and was published last year. Which he's also now applied a dimension to

the southeastern U.S. for the Southeast CSC. It's a fairly complicated approach, as explained

in the paper. It includes a variety of metrics of spatiotemporal variability, of temperature

and precipitation. We use this to guide our selection of models,

tending to recommend we use the ones on the left hand of this figure.

We'll start on the climate model results with the slightly complicated figure. The only

one of these I'm going to show, but I have a reason for showing it. What this figure

shows is the change in temperature and precipitation, the Y and X axes respectively, for this set

of models currently available. The number is the model rank, one is the best

and 30 something is the lowest. Not all models are included because as I noted before, some

models that had 20th century runs which led to the ranking did not have 21st century runs,

and those are gradually being filled in. But generally the numbers in the 20's and

30's models works on the 20th century. The plus symbol is the mean of all of the model

simulations for that RCP. The dark numbers have MACA data available and the light are

not available. The gray shading around both the X and Y axes

are the percentiles of inter annual variability during the 20th century.

It helps us see, for instance, looking at just the X axis variability, that most of

the models say that the future, late 21st century precipitation, annual mean precipitation

will be within the range of variability experienced in the past.

If you look at those plus symbols, they're shifting by only five percent or eight percent.

Some of the models, in the upper right hand corner, you see 11's and 29's, indicate large

increases in precipitation. No models indicate large decreases in precipitation.

I circled the number 15 because for some of the results you'll see we're going to focus

on that model, the MIROC5 model, because it's in the middle of the pack and it's a reasonably

good performer. This is the first of several slides like this

that I will show, where the 20th century simulations are shown in gray, with the all model average

shown as the heavy black line. And then going into the future where RCP4.5

is shown in yellow with one heavy curve ending at about six degrees Fahrenheit warming, and

the other heavy curve for RCP8.5. You notice the RCP4.5 and 8.5 worlds start to diverge

somewhere around 2030 or 2040. And by the end of the century not only are

they five degrees Fahrenheit difference in the multi model average, but notice the slope

as well. The RCP4.5 world has climate starting to stabilize, and the RCP8.5 world is continuing

to change at a pretty rapid clip. There's an overlap between the two that's

indicated in orange, and you can see the range of variability is a couple of degrees Celsius,

or several degrees Fahrenheit. The coolest model down at the very bottom

of the yellow area would give a warming of only a couple degrees Fahrenheit by the end

of the century in the RCP4.5 scenario, whereas the hottest model in the RCP8.5 would have

us warming by about 15 degrees Fahrenheit. Many of the results that I'll show later are

either from my level five, or from the all model average.

This is the same kind of plot for precipitation, and you'll notice that there was very little

change during the 20th century. Even during the 21st century only a few percent

change for the all model average. Now you remember from the complicated scatter diagram

there were a few models that indicated large increases in precipitation.

But the robust message is that there doesn't appear to be a solid indication that precipitation

would change dramatically in the annual mean. We'll come to the seasonal differences shortly.

The MACA data, which are summarized here for the Pacific northwest region, includes other

variables like wind speed. This is the plot of change in the average wind speed over the

northwest. A decrease of roughly five percent for the

all model average for the RCP4.5, but slightly more for RCP8.5. This appears to be because

several models tend to have the Pacific storm track shifting farther north we get fewer

windy winter storms. The diurnal temperature range is important

for many ecological applications, and here we start to see some interesting seasonal

variations. Winter diurnal temperature range decreases, and the summer diurnal temperature

range increases. Notice the Y axes are somewhat different here, the decreases and increases

are fairly comparable in magnitude, roughly one degree Celsius. And this is mostly having

to do with changes in cloudiness, as indicated by this figure.

Now, we don't yet have full results for RCP4.5, so all that's shown here is RCP8.5. But the

winter, shortwave radiation goes down indicating an increase in cloudiness, which is also connected

with the decrease in diurnal temperature range. Then in summer, the reverse is true and a

decrease in cloudiness leads to an increase in incoming shortwave radiation and an increase

in diurnal temperature range. The maximum temperature in June, July, August

is shown here, along with the MIROC5 results. And again, all results have been smoothed