So good afternoon, everyone.
Can we please be seated?
There are a few chairs sprinkled
empty chairs sprinkled up here, a few over here
You'll have to come down front.
We can't have you sitting in the aisles due to fire codes
so if you're saving seats for someone,
we'll have to go ahead and forgo that.
And we're going to go ahead and get started.
So everyone, welcome to our second GreenTalk of Fall
'17.
We have with us today
Forrest Melton.
He is a senior research scientist
with NASA, as well as
Cal State University Monterey Bay.
Went to undergrad at Stanford University.
And I'm going to allow him to tell you
the rest of his background.
My name is Barbara Murphy-Wesley.
I am a fellow 100W teacher,
and I'd like to welcome everybody.
Let's give Dr. Melton a warm welcome.
[applause]
Thank you so much. Nice to see all of you.
I work over at NASA Ames Research Center,
also as a research scientist at Cal State Monterey Bay.
Before I get started -- I don't want to forget this --
if you're interested in working with NASA, interested in internships,
interested in remote sensing, neuroscience,
be sure to google NASA DEVELOP.
The NASA DEVELOP program is an internship program
and we host anywhere between a dozen and about 20 students a day
and it's every summer. Fantastic program.
So my day job is mostly comprised of using
satellite data to understand changes on the Earth's surface.
I do a lot of work on applications of satellite data
to measure different hydrologic variables
and in particular to look at crop water requirements,
crop water demands, and crop use, and agriculture,
as well as to monitor changes in agricultural production.
I also spent about half my time over the last four or five years
working on data sets for the National Climate Assessment
and contributed to an interagency working group
that developed tools to try to improve public access
to those data sets for folks exactly like you:
Engineers, city planners, folks that are trying
to come up with solutions to climate change,
to improve climate resiliency over the coming decades.
Now, unfortunately, I didn't realize that Dr. Cordero
also was going to talk to you about climate change last week.
I hope I'm going to give you some slightly different graphics, different presentations.
I'll touch on some of the same topics he touched on
but I hope to give you some additional perspectives on that.
Two things.
The two most important things to take away from today.
One: Take a look at the U.S. Climate Explorer
for the U.S. Climate Resilience Toolkit.
This is a tool that has been developed by
NOAA, NASA, the USGS., EPA,
working with the U.S. Global Change Research program in the Bureau of Reclamation
to try to increase and improve the ability to access
these data sets in ways that are easy to understand
and easy to create summaries for.
Again, the primary goal is to inform
planning for resilience to climate change,
but it's also a really useful way to get local-scale information on future climate conditions
and potential climate change impacts.
So the URL is there. If you have a laptop, feel free to browse while I'm talking.
I won't be offended. Take a look.
The other key message is, as engineers,
I hope you will read the national climate assessment.
All right?
As engineers, your job day-to-day will be to solve problems.
This is a list of the problems that we will be facing.
All right? So there are potential challenges
for civil engineers, for electrical engineers,
for biomedical engineers, for structural engineers.
If you can think of a problem, it's probably going to intersect with climate change
at some point in time.
The third national climate assessment was published in 2014.
I worked on some of the technical reports
that served as the basis for the chapters on
ecosystem services and biodiversity.
The fourth national climate assessment is in progress.
And the special climate report is now available
there on the NCA website.
So be sure to come back at some point and take a look
at some of these sites, browse through the materials,
at least read the executive summaries for these reports.
I think you will find it well worth your time.
And also, as a technical writing course,
I think this is a great example of technical writing.
Scientists, I can't tell you how many hours and hours
we spend trying to get the phrasing right,
reducing hundreds and hundreds of papers
into the most important points and graphics.
So this is a nice example of how teams of scientists
work together to write.
If you read it and you think it's unclear, let me know.
If you think you can do better, I hope you'll have a chance
to get involved in the future.
But in terms of a technical document that's written for the public
and tries to translate detailed scientific and technical information
into understandable and usable formats and key bullet points,
I think this is a really nice example.
And again, well worth some time exploring
what's available in the national climate assessment.
So what I'd like to do today is talk a little bit about
climate change, causes, impacts, and solutions.
And then I'm going to touch on some few examples
of how we produce data sets for the U.S. Climate Explorer
and the national climate assessment.
And then I'll give you some examples and walk you through some of the features
of the climate explorer itself.
So first, causes.
You guys have probably all seen a slide like this one
here on the right, in terms of the total U.S. greenhouse gas emissions
by economic sector.
I'm guessing Dr. Cordero showed you something like this.
I think something we don't do well as humans
is envision large numbers and their impacts.
Right?
So each year we burn, globally,
close to 33 billion barrels of oil,
8.6 billion tons of coal, and 120 trillion cubic feet of natural gas.
Those are big numbers. It's hard to get your mind around them.
As an engineer, when you encounter a big number,
I would encourage you to translate the units.
Convert it to something that's easy to visualize
and gives you a sense of the number
and the scale of the problem you're trying to deal with.
So for oil, how many of you have driven from
Bakersfield to Butte, up and down the Central Valley?
Anyone? Or just driven across the Central Valley
going over to Yosemite or Sequoia or up to the Sierra
at any point?
It's big, right? 22,500 square miles.
If we took all the oil
that we burn each year, you would cover that ankle deep
about four inches deep across the entire Central Valley.
Every year we flood that, light it on fire, burn it.
8.6 billion tons of coal, that's enough to cover
all of West Virginia about four inches deep
in coal. Think about that.
And we burn enough natural gas each year to cover
the entire U.S. with a layer of natural gas one foot deep.
And in addition, we also, over twelve years,
from 2000 to 2012, we burned about 370 million acres of forest.
That's an area equivalent to Texas, California, and Arizona combined. Little bit of good news:
the net rate of forest loss between 2000 and 2015
was half that we experienced in the 1990s.
So progress in that area.
But still, I want to try to give you some sort of spatial sense
as to how much fossil fuel gets burned each year.
And then this is the distribution of greenhouse gas emissions by sector globally.
So similar to the U.S. but a little bit different.
Another thing that I think we take for granted
is the size of our atmosphere.
When we stand on the surface of the planet and look up,
it feels infinite, right?
You can see all the way to the stars, the moon.
We forget that the atmosphere itself is really this thin,
narrow blanket that shields the Earth.
The troposphere itself, where most of our atmosphere is contained,
is only about 11 miles deep and all the way up
to the stratosphere is just 30 miles.
So when you see it from space,
you realize just how thin our atmosphere is.
And when we burn most of our fossil fuels
we don't see those emissions.
They're going through catalytic converters.
They're going through different filters to take out
the particulate matter, take out the aerosols.
This is a shot from the Deepwater Horizon oil spill.
And there we're looking at -- that was about
five million barrels.
Keep in mind we're burning close to 33 billion barrels.
What we're looking at is probably
a couple thousand barrels, maybe tens of thousands
of barrels that are on fire.
You can see the plumes that you generate.
So when you are able to see the particulate matter,
you get a sense as to how much carbon dioxide
is being emitted across the planet
and how quickly we actually can fill up our atmosphere
and change the composition of greenhouse gases in the atmosphere.
The total heat being trapped by these gases
is equivalent to detonating about
four nuclear bombs the size of the Hiroshima bomb
per second, all year long.
So that's how much extra energy we're adding to the Earth's system
by burning these fossil fuels
and through the loss of carbon from deforestation.
Okay? So...
we've been doing this for a long time.
Since the 1950s we've been measuring this
at a network of stations around the world,
starting with Mauna Loa.
This is the famous Keeling Curve. I'm guessing Dr. Cordero showed this to you.
So we've seen, since the pre-industrial period,
about a 40% increase in greenhouse gases.
The oceans helped us out.
The oceans have absorbed about roughly 30% of the total emissions.
This has reduced the accumulation of greenhouse gases
in the atmosphere, but of course, that's also contributed
to ocean acidification.
These concentrations are unprecedented
over the last 800,000 years.
So Ice Ages, all of the natural variability and fluctuations,
we have not seen concentrations on these levels.
And we're headed for even more.
So you are roughly here, right now.
We crossed the atmospheric threshold of 400 parts per million CO2 as a daily average
for the first time in May, 2013.
We got to close to 407 parts per million globally
earlier this year, in about April.
As of June, we're at about 406 parts per million
so there is that annual cycle or increase and decrease
depending on whether you're in the northern or southern hemisphere.
We are now growing or increasing our atmospheric concentrations of CO2
by about 3 parts per million, 2.8, 2.9 parts per million per year.
So we're on track to hit 415 parts per million
by 2020, and unfortunately, that puts us right on this high emissions scenario.
So this is our business as usual scenario.
When we talk about uncertainty in climate change,
the biggest driver is what people are going to do.
That's the thing that's hardest to model
and hardest to capture accurately.
So to do that, the climate modeling community
develops these scenarios, which we call representative concentration pathways,
which entail assumptions about how human society is
going to manage energy and manage
natural forests going into the future.
And we developed these scenarios.
Our best achievable or our best case
put us as about 420 parts per million.
Again, we're on track to hit that by 2020.
It's probably off the table.
At the moment, we're tracking a scenario
that's going to put us closer to about 900 parts per million of CO2 by the year 2100.
So that's our business as usual scenario
or our high emissions scenario.
I did want to show you a quick video,
just to put this in perspective as to how stable
this is over time. Let me jump ahead to save time here.
So this is this curve. This is another representation
of the curve I just showed you.
This is going back looking at CO2 concentrations
from ice cores, bubbles from ice cores.
This is going back about 1000 years here.
This is from the Law Dome ice core.
and then there's a whole series of ice cores
that go further and further back, all the way to 800,000 years.
I just want you to get some idea as to where we are now
given the historic variability.
You can see what variability typically occurs
between glacial and interglacial periods
going back 50,000 years, 60,000 years
70,000 years.
We're getting up to 100,000 years.
Here we're going to switch over. This is the Vostok ice core.
We're going to switch to the EPICA Dome C ice core in just a minute.
So you can start to get some sense as to how stable things have been overall
and how unique these atmospheric conditions are
at present. This is going back about 800,000 years total.
So hoping to try to give you some additional sense
of the scale of these things.
Here we are today. This is circa 2008.
This is this historic record we just showed.
This is our best-case scenario, sort of what we would consider our best achievable case right now
around 500, 550 ppm by the end of this century.
This is going all the way up to 900, business as usual,
assuming no coordinated global action was taken.
The other really important point for you to consider
as engineers is we don't just stop emissions
at 2100, right?
We're still here on the planet, hopefully,
still have a strong, growing economy.
There will be continued emissions from agriculture,
from land use change, and from, presumably,
some continuing use of fossil fuels.
Even if we did stop those emissions --
this is an experiment where they simulated emissions
from 1900 to 2100 and then turned them off --
they persist in the atmosphere for centuries.
This is the residence time of the CO2 that's emitted
another 300 years beyond 2100 to about 2400.
And this is the predicted temperature changes --
global temperature average -- as a function of those emissions.
So the decisions we make today,
whether we develop solutions or don't,
will affect us for centuries.
So not only our kids and grandkids
but their kids and their grandkids.
So, really important. In total, about 15 to 40%
of the emitted carbon remains in the atmosphere
for the next 1000 years.
So let me turn to impact now.
Why does it matter to engineers?
What are the types of problems that you might
be faced with solving in your careers?
Engineering, I think of, as the discipline of solving problems that affect people.
So when you look at the national climate assessment,
it is your guidebook for job security.
These are problems that are likely to require engineering solutions
for a long time.
Here's the headline from the last IPCC report.
This is the consensus statement from the global science community.
"Warming of the climate system is unequivocal,
and since the 1950s, many of the observed changes
are unprecedented over decades to millennia.
The atmosphere and ocean have warmed,
the amounts of snow and ice have diminished,
sea level has risen, and the concentrations of greenhouse gases have increased."
I'm most often asked about uncertainty.
How certain are we in the models?
I touched on some of the differences in greenhouse gas emission scenarios.
The climate science community continues to work and improve the models.
There's questions about, what's going to happen to extreme events?
How will precipitation change?
How quickly will ice melt at the poles?
What are the feedback loops that we haven't discovered?
So those are all things that the community's actively working on
and trying to understand.
But at the end of the day, I can't think of another issue
where there's consensus among 195-- hopefully still 195 countries--
The CEOs of British Petroleum and Royal Dutch Shell, the Pope, and the science community
that urgent and immediate action is needed.
Right? So there is a strong consensus.
Whatever the U.S. government does over the next few years,
the global community will be moving forward
on changing the ways that we manage-- produce, manage, and use energy
and we will see solutions to climate change developing globally.
So it's important, I think, for engineering students to follow these developments,
be aware of them,
and think about how they might influence your choices as you proceed with your career.
So some of the key impacts.
The most directly observable one, other than the changes in greenhouse gas concentrations
in the atmosphere,
global temperature, right? And this is why we call it global warming, of course.
These are the observed changes in surface temperature
from 1901 to 2012.
Globally-- about a degree total on average,
similar to what we see in the U.S.,
about 1 degree celsius, 1.8 degrees Fahrenheit
across most of the U.S.
Higher warming there, especially at our higher latitudes in the northern hemisphere,
but also some additional warming there in the southern hemisphere.
All right, I think Dr. Cordero touched on this,
I just want to emphasize this point.
If we don't account for the greenhouse gas emissions,
we can't reproduce these with a physically-based general circulation models,
that we also refer to as "global climate models."
So when you run the models without
the increasing greenhouse gas concentrations, this is the projection you get.
When you run them-- forcing from greenhouse gas emissions
we are able to reproduce the observed
global temperature patterns
over the last, roughly, 100 years, right?
So without accounting for the greenhouse gases, we cannot explain the observed warming.
I think Dr. Cordero may have shown this, but again,
this is showing the net contribution to temperature change
from fluctuations and total solar radiation,
emissions from volcanos,
and then human-associated emissions.
So again, that human-caused driver really explains most of the observed
changes in temperature.
Another thing to think about as you're developing models as part of your career,
as many engineers do, is how good are they, right?
You want to test your predictions,
and a good way to do that is to run models over a historic period or make a forecast
and then compare it to the observations.
Right? So when I was a student sitting in an auditorium --
not unlike this one -- there's a couple of things we were learning about.
Global temperatures will continue to rise, polar ice caps will melt.
At the time, those things were not happening.
Okay? When I started studying climate science and climate change,
and earth systems, we were, I hate to admit it, right about here.
In 1990s. Since then,
every-- the last three decades have all been warmer than the previous decade.
16 of the last 17 years have been among the warmest on record.
So accelerating warming over the last few decades.
And you can begin to see the accelerating increase in global temperature and warming.
The other prediction was that polar ice caps would melt.
And here's our trend here from 1978 to 2015.
This is the length of the satellite data record that we have for these observations.
as well as a couple of some ground-based measurements.
So that's the change in Arctic sea ice. So overall, models definitely capturing
some of these major trends, things that
without models, we would not have been able to anticipate
or predict.
Another thing, I noticed, that we're not very good at,
as people, is detecting trends around us.
Things that are-- if we look at these graphs, right?
It's obvious what the trends are. Think of it not as some
anything to do with climate-- think of it as a stock.
If an advisor had told you to buy Apple in the 90s,
and you saw this type of pattern, you'd say, "Hey, that guy was really
prescient," right?
Really insightful. If they had told you to sell Pets.com, and then you saw a trend like that,
you would say, "Oh, yeah.
They understood it. They knew what was coming."
So when we think about these trends and we graph them,
it's much easier to see the pattern than it is, say, just observing
weather variability, day-to-day variability and what surrounds us each day.
We're not so good physically at picking out those trends.
We notice the extreme events
but until we start to plot and graph the data, it can be much harder to detect
the trends in any long-term conditions.
Another animation of Arctic sea ice,
just sort of showing you what this is looked like.
Again, there's natural variability. There's inter-annual variability
and minimums and maximums of sea ice extent,
but when we look at the long-term trend here over the last 35, 36 years--
really clear trends.
And these are actually happening a little faster than
we would have thought in the 1990s.
So this acceleration of sea ice melt is definitely one of the things
that's concerning and may represent a tipping point.
And then to the point about stability. We talked about C02 concentrations
through the best of our ability to reconstruct
temperature going back through human history to about 200 A.D.
Again, very stable temperature patterns overall.
And when we look at the 1 degree Celsius, 1.8 degree Fahrenheit,
an increase in global temperatures is something that is
unprecedented in human history.
And again, we are about here.
And when we look at our best achievable scenario, our best-case scenario
that we use in IPCC reports and the National Climate Assessment,
again, this would have us just stabilizing at about 420 parts per million CO2,
and we're already at 407.
The target now is to try to stay below at about two degrees Celsius,
which would put us in this range here. We call this RCP 4.5,
and out worst case is up here, about RCP 8.5.
Anybody know what those numbers stand for, by the way?
And what the RCP numbers--
How many of you know what the RCP numbers--
Okay, I'll take a minute and explain about that then.
We measure the effect of greenhouse gas concentration levels on the atmosphere
and they're radiative forcing in Watts per meter squared.
So these numbers are the additional energy
that's trapped-- additional solar energy that's trapped in the atmosphere
measured in Watts per meter squared.
So this is akin to-- 8.5 is 8.5 Watts per meter squared
of additional radiative forcing globally.
That's like putting out 8 1/2 Christmas tree lights
on every square meter of the earth's surface
forever-- for the foreseeable future.
That's how much extra energy is being trapped by the atmosphere
as a function of the greenhouse gas concentrations.
I want to come back and touch on this in a little bit.
Sometimes it's hard to think about, "All right,
five degrees Celsius. You know, nine degrees Fahrenheit...
in winter that might be kind of nice, you know?"
Or if on a cool fall evening, you'd think, "Oh, it'd be nice if we had a balmy day here."
But I want to show you what these temperature projections
look like when you impose them at high spatial resolution.
The general circulation models that we run at the--
these are the global climate models that are used as the basis
for all of our climate reports--
they run in between one and two degrees currently,
and that's kind of what it looks like.
Engineers, city planners-- you have to work at local scales.
So you need information that's specific to your city, your community, your county.
To do that, the science community has been trying to combine--
or has been combining
these coarse resolution global data with high resolution historical data
to improve local scale information.
So a few years ago, we produced the first
really local scale data sets for the U.S.
These are at 800 meters or about half a mile per pixel,
so really one scenario for every city, town, and county in the country.
There are 34 different models. We looked at all four possible
representative concentration pathways. There's about 17 terabytes of data.
I'll come back to this in about 10 or 15 minutes,
but I want to show you what these look like.
So this is the historic data. This is 1950. This is the typical
maximum daily temperature in July.
This is the average for July. This isn't the heat waves,
this is just your typical high temperature for July, 1950.
I want you to-- 105 years, the upper end of this scale,
I want you to remember that number. I'll come back and explain
why it's important in a minute. That's about 40 degrees Celsius.
Notice that the only place that really typically experiences temperatures like that
historically is the sort of the desert southwest,
the area around Phoenix, Tucson, Death Valley.
Let's see how these patterns change.
So this is 1960... 1970...
1980... 1990.
You start to see the warming that we saw on those graphs earlier kick in here.
1990... 2000...
2010.
Now we move onto the projections.
So this is what the climate models tell us is likely to happen
under our business-as-usual scenario, under that RCP 8.5-high emission scenario.
2020... 2030...
2040... 2050...
2060... 2070...
2080... 2090...
2100.
So without action taken, that's where we're headed.
That's our business-as-usual scenario.
What do you notice?
All of a sudden, we're all living in Phoenix.
And what else do you notice?
Where are our big agricultural areas?
Right? Central Valley...
Right? Great Plains, central U.S., right?
So all of our big agricultural areas start to have temperatures above 105,
40 degrees C on a typical July day, August day.
Again, never mind heat waves, never mind extreme events.
This is what you would expect for averages.
So we don't have to go there, either.
This is what it looks like if we-- well, this is now kind of hard to achieve,
but if we were able to stabilize atmospheric CO2
around current levels, around 420 parts per million,
this is how the temperatures would look like in July.
So, really big contrast.
So that's why if think about five to nine degrees Fahrenheit,
this is what we're talking about,
and this is why the science community is concerned.
Okay, these are the comparisons there.
All right, so why 40 degrees Celsius? Why does that matter?
So this is a graph, a chart
of temperature effects on crop production. These are our major
stable commodity crops here.
Corn, sorghum, beans, cotton, peanut, rice, soybean, tomato.
This is the failure temperature for reproductive yield.
Are any of these numbers above 40 degrees Celsius, 105 degrees Fahrenheit?
No, right?
So that's not good news.
When we look at the projected change in crop yields in 2050 from the World Bank,
this is what we see. And notice, again,
these yield areas that start to correspond with
higher and higher average summer temperatures
are associated with the predicted yield to decline.
This is an area of active study for the climate science and agricultural community.
For those you interested in bio-engineering and biotechnology,
temperature-resisting crop varieties and improving water use efficiency in crops--
guaranteed job security.
So, anyway, this is concerning.
Precipitation. The models have a harder time with precipitation trends.
A lot more inter-annual variability, a lot more spatial variability.
This is one of the key areas of research in climate science and in our down-scaling work,
our ability to produce accurate local-scale information on climate change.
These are the observed precipitation trends
over basically the last century, 1901 to 2006.
You can see some long-term trends towards dryness in
southern half of California, southwestern U.S.,
some of the overall-- maybe a slight increase in the northeastern U.S.,
but among strong trends relative to the observed inter-annual variability.
But keep in mind while we're thinking about agriculture,
almost 60% of our irrigation withdrawals come from surface water, right?
So if we nudge this even a little bit,
that's going to affect our ability to supply agriculture with water.
And we've gotten a taste of that, and I'll touch on that in a few slides.
Similarly, increases aren't always good, either.
Increases in extreme precipitation events as we've seen over the last couple of months--
it can create enormous challenges, especially for large urban cities,
but also for rural populations.
This is where we're headed.
These are the projections-- these are [unclear].
This is the old nomenclature.
B1 is our lower emission scenario, A2 is our higher emission scenario.
California in general, we are roughly about here.
California in all of these scenarios tends to sit kind of right on the border between
areas that are likely to get little bit drier and areas that may get a little bit wetter.
So a lot of uncertainty about what's going to happen overall for
for average precipitation in California.
As we've seen in California, average precipitation doesn't matter.
If we take 2015 and 2017, about average year,
but huge impacts for agriculture, huge impacts on water supplies,
huge impacts on communities that were affected by rapid declines
in groundwaters in the Central Valley,
and then flooding, and impacts to reservoirs, to the Snowmageddon over this last year.
When we look at the predicted increases in extreme events--
I'm sorry, this is the observed increase in extreme events from 1958 to 2012,
not much change for the southwestern U.S. where we are,
but big observed increases in the frequency of these heavy extreme events
in the northeastern U.S. as well as the southeast
and the central U.S.
When we looks toward the future, this is what we get
under a lower-emission scenario
and then under a higher emission scenario.
So the higher emission scenario, we expect an increase in these
really heavy rainfall events,
the return interval of the 20-year extreme precipitation
across the board in the U.S.
So that's a new finding from the USGCRP Climate Science special report
that was just published a couple of months ago.
Still in draft form, the but the text is pretty much finalized.
For California, if you're thinking about engineers and
especially for those of you who are civil engineers,
what we really care about is not just the precipitation,
but whether it falls as rain or snow,
and also when it melts and runs off the mountains.
And we so dependent
on using our winter snowpack as essentially this massive
free natural reservoir.
What we've observed from 1948 to 2002 is this increase in melting.
So that snowpack on average is melting earlier and earlier each year.
You can see it in a number of locations.
It's anywhere between 10 to 20 days earlier over the last 50 years.
When we look towards the future, that gets even worse.
So our projected snow-water equivalent
stored in that winter snowpack under our business-as-usual scenario
is about 60.
By the time we get out to the middle of the century, about--
a reduction of about a third to 66%,
and by the end of the century, a reduction of more than half.
So that drives changes in the timing of runoff,
soil moisture and vegetation water stress, so all of that water that used to
melt gradually from the snowpack throughout the summer isn't available
for a higher altitude mountain ecosystems anymore.
It's going to change evapotranspiration patterns,
the timing of plant growth,
and it has big impacts on the demand for water storage
and the availability of water storage,
as well as for fire return intervals, fire risk,
aquatic ecosystems, and on and on and on.
So for those of you thinking about civil engineering,
really important to pay attention to what's going to happen to
our water supply, infrastructure, and how climate change affects the timing
of runoff and the snowpacks stored there in the Sierra during the winter.
California ag, also highly vulnerable to climate change.
A couple of years ago,
net returns of more than $40 billion a year in cash pharmacy--
it's about 770,500 individual farms
We're a huge supplier of domestic and international--
huge supplier of both domestically and internationally.
For specialty crops we produce about half of all U.S.-grown fruits, nuts, and vegetables.
So California is a really important part of the U.S. food supply system
and our infrastructure for producing and distributing food.
And we grow about 400 different crops in the state.
And these are primarily for the commodities, not as much for the specialty crops.
We talked about some of the global patterns earlier,
I wanted to show you what the predictions are for California.
Alfalfa looks okay but when we look at corn, tomatoes, rice, wheat, cotton, sunflower,
again, that same pattern that we saw on the global data
being echoed locally for California
with predicted declines over the next 50 to 100 years in the average annual yields for those crops.
We're also starting to see a few things that are kind of concerning
and while we can't directly attribute these to climate change itself,
but it is a signal that we need to monitor.
In 2012, we observed reductions in peak growth in most of the major ag regions in the world.
Across the Central U.S., Argentina, Brazil, Ukraine,
even in India, which we originally thought was alright.
Once we looked at the actual precipitation anomalies,
the clouds that we saw in the satellite data
weren't actually producing rain.
Heavy cloud, no rain.
So even India was experiencing significant droughts in 2012.
Those combined, concurrent drought events
and reductions in crop production
resulted in another spike in crop prices in 2012.
And since this was the first time'd seen two large spikes in commodity prices globally
within the span of a decade.
So another concerning signal.
Just to see what that'd look like in California:
this is 2015, this is the amount--
these are maps of crop versus fallow land in the Central Valley.
Although we saw an increase of about half a million acres,
this is using satellite data,
we're able to detect an increase of about half a million acres
of land that was fallow during the drought relative to the normal years.
Wildfire risk, I mentioned this earlier.
These are the projections for the increase in wildfire risk under our business-as-usual scenario.
Here on the right,
so we can see throughout the Sierra
and along the coast range,
even here in the Bay Area and around Monterrey Bay
it projected increases anywhere between 200 to 400% in fire risk,
decreases in the fire return interval and then potential increases in fire severity.
Here again we have our higher emission scenario, A2,
our lower mission scenario,
and we can start to see the average increase in the risk ratio is four to five-fold.
400-500% increase in risk
relative to 1981 to 1990, and moving out towards the future.
For those of you thinking about material science,
the need for new building materials, I think Dr. Cordero touched on this,
different ways to design homes, roofs, building materials to be more fire resistant.
Another important topic.
Aerosols and aerosol management,
ways to design cities to be safe and effective
for folks that have susceptibility to aerosols and asthma
and other important considerations.
So when we see these fire events, you get a census to how destructive they are.
Increases in electricity demand. Did Dr. Cordero touch on this last week?
Yes? Yes. Okay.
So we'll skip this one then.
Another one. Sea level rise.
Did he touch on this one? Have any of you guys seen this figure?
Audience: No.
Melton: This is a good one for us, right?
So sea level rise gets us two ways.
One, just a gradual increase.
Second, it exacerbates storm surge.
So when you look at the combined effects of sea level rise and storm surge,
it's an impact of a one meter-rise in the SF Bay Area,
these are all the areas that would be affected.
We've already seen roughly about 20cm of increase
over a little more than the last 100 years in sea level.
So this is where we are today,
and the scenarios put us anywhere between one to four feet.
The new National Climate Assessment, our upper limit used to be about 6 feet,
the new scenarios for the National Climate Assessment now include a scenario of up to 8 feet by 2100.
And this is part in response to the accelerated increase in reduction in Arctic sea ice.
For us here locally, this is a big one, right?
So how we start to design infrastructure to be resistant
to increases in sea level and increases in storm surges is really important
This is most of our economy here.
Google and Facebook, a lot of the tech companies have offices here.
So ensuring that we can protect and sustain that infrastructure
is really important to our local economy of course.
I don't want to leave you with only bad news
so I want to say a few words about reasons for optimism and hope,
not least among these reasons, as all of you in this room,
again, having bright, talented, energetic folks interested in developing solutions
to problems and aware of the potential impacts of climate change,
I think is really valuable.
I hope you look at this as an opportunity
and not just a reason to be depressed.
So I really like what John Holdren had to say,
which is that we basically have three choices when we think about climate change.
We have mitigation: reducing greenhouse gas emissions,
thinking about better ways to produce and use energy,
we have adaption, which is what the engineers--
a big part of what engineers and civil engineers will help us do,
and suffering.
And we're going to do some of each, and the only question is
what the mix is going to be.
Obviously the more mitigation we do, the less adaptation will be required,
and the less suffering there will be.
Have you guys seen this figure before?
Anybody? Who's seen this figure before? Anyone?
This is the good news. This is the best news.
This was a study that was done by the McKinsey consulting company
about the cost of reducing greenhouse gas emissions.
Everything here on the left has a net economic benefit,
and then everything on the right would have some cost given current technologies.
New technologies might reduce those costs. So this is the net benefit.
And the good news is that there's a whole host of things
that we can do today, and are doing in many places,
that not only reduce greenhouse gas emissions
but have a net economic benefit,
like cost savings or an increase in economic activity.
So there's things like switching to LEDs,
improving appliance efficiency,
hybrid cars,
changing the way we till and manage land,
changing the way we manage rice production,
increasing the use of small hydropower systems,
changing the way we till and manage residue on crop lands--
a whole host of things that are all solutions,
and then there's a whole other set of solutions
that have relatively low cost.
Many among them, these relate to energy efficiency.
I think Dr. Cordero talked about this a fair bit.
One thing I want to emphasize about this point--
this is a curve of residential electricity use per capita from 1960 to 2009.
Here's California.
We're, due to some really smart effective policy--
good building codes, good practices that encourages
use of efficient appliances and efficient building materials,
we've pretty much stabilized.
Here's the rest of the country.
How many of you feel like you're short on electrons?
Deprived of electrons? Can't charge your phone?
No? No one, right? So that's the good news, right?
California serves as a model for what other states
and much of the rest of the country can do
to reduce greenhouse gas emissions without depriving anyone.
So other good news: we see the rapid decline in solar price trends.
For those of you interested in material science, a fantastic field to get into.
So it's now at the point where the cost without subsidies
is starting to approach the cost with subsidies.
And then, of course, we're seeing this dramatic increase and acceleration
in the employment of solar,
especially here in California.
So a large increase in our utility and local-scale photovoltaic capacity.
One more reason for optimism:
here are two breakdowns of
total production of electricity by source.
On the left is a country which is 33% renewable, 21% nuclear,
so more than half non-fossil fuels.
On the right is a country that is 9% renewable, 8% nuclear.
Anyone want to guess what the country on the left is?
Come on. Give me-- France? Sweden?
Netherlands? All good guesses.
How about on the right? What's the country on the right?
U.S. Okay, that's an easy one.
All right, so the one on the right is U.S., we got that one.
Spain! Spain is on the left.
It's one thing to get beat by Spain in World Cup soccer, totally fine with that.
It's another thing to get beat-- who makes the best paella?
Okay, fine, we'll give Spain that one.
But innovation and energy production? No.
No, we can't lose in that area.
I think there are examples from Europe on how we do this effectively
without negatively impacting the economy
or the experience of any of us on a day-to-day basis.
Okay? So again, good news.
And so really we have this question about kind of which future
do we want to pursue? Which future do we want to live in?
And the really good news is that it's up to each of you.
When we look at lot of the solutions--
the new technologies, engineers, and scientists have a role in understanding
developing, building, and testing
and deploying and marketing these technologies.
So I think really good news about the types of options we have available to us
and the growth in
technologies that are both energy-efficient but also fun to use--
fun and effective to use.
There's a challenge, though, for engineers.
And we've seen some really nice examples just in the last year.
So on the left, we have the Oroville Dam,
on the right, we have Houston after Hurricane Harvey.
Here's the challenge:
when you're building things, especially on the civil engineering side,
the standard practice for a long time has been to look at historical averages
to guide your design criteria, right?
And we can't really do that anymore, right?
Houston has had three 500-year events
in the last three years.
We had record-setting drought
followed by record-setting snowpack-- you know, the Snowmageddon.
So we're swinging back and forth, from one extreme to the other.
That's really hard on engineers who are tasked to thinking about
not only do I have cost constraints,
but how do I design a building or a structure or a canal
or a levee or a dam to be effective under average conditions,
but also to hold up under extreme events that we may never have seen before?
Good news is that the climate science community has heard that.
We're working on trying to produce better and better data sets,
both for the National Climate Assessment and the U.S. Climate Explorer
to make it easier for folks to try to get a handle on what the future may hold.
Our own work at NASA Ames
uses the NASA Earth Exchange, which is a collaboration between
the earth science division and the supercomputing group there--
the supercomputing division at NASA Ames,
and we've taken a lot of the global satellite data sets
earth observation data sets, outputs from global climate models,
brought them all together on the supercomputing platform
to make it easier for us to accelerate science
to understand changes in the earth's system
and develop data products that are intended to help assist with
climate resilience planning and guide decision-making.
So this is a capability at Ames.
If folks are interested in high-performance computing, interested in earth science,
interested in engineering,
please come talk to me. Let me know.
I touched on this earlier.
So the way we translated the figure on the left
to the figure on the right is we used
55 years-- 56 years of gridded historical weather data
to capture the spatial
and temporal distributions of temperature and precipitation
in spatial scales that are much, much finer than we can do on the GCMs.
We then used the supercomputers
to calculate a set of correction factors for temperature and precipitation
that allowed us to correct for any local-scale biases in the global models
and then disaggregate each of those one-kilometer cells
into basically--
sorry, the one-degree cells, 100 kilometers by 100 kilometers,
into these one-kilometer cells, roughly half a mile by half a mile.
Again, we did 33 of these global models,
four representative concentration pathways.
And then this data is available in multiple different ways,
and I'll touch on a few ways to get access to it.
But these give you local-scale monthly scenarios
for understanding potential changes in temperature and precipitation.
Two other data sets that are also
one of the bias-corrected constructed analogs
was developed by the Bureau of Rec,
also on the supercomputers at NASA Ames.
These give you daily temperature and precipitation projections
at a spatial resolution of about 12.5 kilometers by 12.5 kilometers,
so 7.8 miles by 7.8 miles.
So for engineering, monthly averages are great,
they kind of give you sort of the broad picture,
but what you really want is what's going to happen with daily conditions.
That helps us understand the extremes.
Recently released, just two years ago, the locally-constructed analogs.
These are similar.
These also use bias correction and constructed analog approach.
These reduce the spatial resolution to about 6 1/4 kilometers, that's 1/16 degree,
and they provide significantly improved projections for precipitation at local scale.
That's the main advance.
These data and the local data sets are not yet available in the Climate Explorer,
but they will be released here over the next month or two.
Knock on wood.
So in the past,
the climate science community has produced archives of data
that were largely held on high-performance computing resources
or stored by scientists locally,
and that is changing.
The rise of cloud computing,
the availability of large data storage and high-performance computing
I think is really changing the paradigm and the way we think about
not only producing these data sets but how we distribute them.
So right now, data sets that we produce at NASA
and other data sets that are produced at NOAA
and some of the regional climate modeling centers
and national and global climate modeling centers
are still archived on resources like the NASA Center for Climate Simulation.
These big, massive scientific data archives
also put on web-based data distribution systems,
like the Lawrence Livermore National Labs Green Data Oasis.
That's a great resource
if you want to download the full data set
and do simulations or do model analysis yourself.
Great place to go and get that data.
Also now making the data available on the Amazon Cloud.
So it's there and accessible if you want to write scripts or run models
and directly access that data without downloading even a single gigabyte.
And also through Google Earth Engine.
So additional cloud-based computing interfaces
that are designed to make it much, much easier
for folks to access and work with data.
Now the step that's occurred really just in the last four or five years
is the distribution of these data sets
via publicly-accessible web-based systems.
I'm going to just touch on two:
the Climate Explorer for the U.S. Climate Resilience Toolkit
and Cal-Adapt.
Strongly encourage you to go and take a look at these sites.
I'll make sure that the links to them are available on the GreenTalks website.
But these are tools that are--
have been developed and will continue to be improved
to increase access for folks like yourself to these data sets.
Here's the Climate Explorer. If you have a laptop or a tablet,
go ahead and pull it up.
You can just Google "Climate Explorer."
It should come up. It's version 2. The full link is down here as well:
toolkit.climate.gov/climate-explorer2.
If you need a minute to take a breath-- Take a breath and focus here on this part.
If you skipped or zoned out for part of my talk,
this is the part that I really want you to
walk away understanding and knowing how to use.
Here are the results for San José, CA.
So we have now climate scenarios for every county in the U.S.
So this is for Santa Clara County and San José, where we are here.
And these are the temperature-- mean daily maximum temperature projections
for Santa Clara.
On the left, we have in the gray area here--
This is what we call the hindcast, the model hindcast
for historical experiments.
So we run the models from 1950 to 2005,
and then we can compare them--
These are the bias-corrected downscale models.
We can compare them against the observed climates.
So these dark gray bars are the observed temperature conditions
from 1950 to about 2010, 2011.
And we can see that the models, as an ensemble, are doing very well
at capturing the inter-annual variability for San José, right?
Increasing our confidence.
We can also track how they do as we move into the future to see
are the model projections--
do they encompass the observed climate as we move into 2020 and 2025?
We have two different scenarios here. We have the lower emissions scenarios.
This is RCP 4.5. This is considered-- that's that
roughly two, two-and-a-half, three degrees of warming-- Celsius.
This is our best-case condition at present.
And the red lines here
are our business-as-usual scenario.
This is that three to five degrees Celsius of warming, five to nine degrees,
maybe even ten degrees Fahrenheit warming.
The dark lines here are the medians,
so this is the statistical summary of all of the climate models that were run.
And then this gives you the distribution.
Some measure of the uncertainty or the range of future conditions
you might have to account for,
in designing infrastructure or building new products.
You can download these as graphs.
You can download the data from these graphs directly.
In addition to the mean daily maximum temperature,
there's annual monthly and seasonal summaries.
Mean daily temperature,
minimum temperature projections,
information-- some information on the extreme days above 95 degrees,
days that the minimum temperature is below freezing.
You can also see maps here. You can change the--
You can slide through time from 1950 to 2090
and see how spatial patterns and temperature
have changed over the last 50 years, or not changed, as the case may be.
And how they're anticipated to change in the future,
similar to the types of animations I showed you a few minutes ago.
Can also look at extreme events.
So this is the graph for Santa Clara county.
The increase in days with maximum temperature above 95
under our best case scenario, not too bad, so, again, here's our historical conditions.
This is our best case scenario.
Under our business, as usual, it's grim, right?
We had that heatwave a few weeks ago. You know how uncomfortable it can get.
Worst-worst case, we're looking at potentially 2-3 months like that each year.
The ensemble is somewhere around 30 days per year.
Ensemble meaning somewhere around 30 days of temperatures above 95 degrees
for our local area, so important to think about as you're considering maybe building designs
HVAC systems,
how you would scale some of the HVAC systems for large buildings,
how you might think about designing cities to reduce urban heat and other effects.
Again, looking at maps. You can see this data spatially through the climate explorer.
See the patterns, see the effect of the coastal cooling, ocean cooling,
coastal fog on our coastal systems,
projections and changes in days below freezing, as you would expect,
rapid declines-- actually, what's interesting here is we see declines in
the days with minimum temperature below 32
in both scenarios, so this is something that seems to be consistent
both under our high and low emissions scenario for Santa Clara county.
Precipitation.
Here we can see what I was talking about earlier.
A lot of variability, much more variability around the model
ensemble medium, then for temperature, but overall this is what's important.
When we look at the historic projections, they seem to do a reasonable job of capturing
the observed inter-annual variability.
When we look towards the future,
overall, not a strong trend,
but we do start to see some increase here
in the extreme events,
so at the upper end of the range from all of the models,
evidence that we might expect increases in extreme events
and heavy precipitation events.
Also look at days precipitation above one inch.
Another way to do this data or to try to get a sense --
you can also display not only the actual values, but you can toggle back and forth
if you want to look at actual anomalies.
Here's a map of the percent change in precipitation
for January for California and the southwestern U.S.
One of the last features I wanted to mention for the Climate Explorer
is you can also go and-- you can use it as a quick and easy way
to access information from weather stations,
so you can actually go and check and see how our
how's the year-to-date precipitation, how is temperature
for Santa Clara county?
What's happening? Does this appear to be an extreme year?
Is it following the average?
Where are we at? So here's for precipitation, we can see
2015, that really dry year,
followed by 2016, close to average,
and 2017, which started off well above average.
So the ability to get direct access to observations
that you can use to compare to longterm averages
as well as the climate projections.
The other website I want to mention is Cal-Adapt.
You can again, just Google "Cal-Adapt,"
I encourage you to take a look at the beta version.
It might now be released, I haven't checked.
Beta.caladapt.org.
In addition to information about temperature and precipitation for California
it also includes some really nice summaries of potential changes in snow pack
potential changes in wildfire conditions,
One thing I want to mention about the Climate Explorer is that
it's intended, in its current form, as a starting point.
And we're hoping, as long as there continues to be support for it from
federal government, and as long as NASA, NOAA, and the U.S. Bureau of Rec, and EPA and the Parks Service
can all continue working together,
on the toolkit, we'll be able to add additional variables, so some of the variables
and some of the information that's available
on Cal-Adapt will also be available nationally
in the future, but especially for snow pack
and information on changes in wildfire, Cal-Adapt is a fantastic resources.
And the folks that have worked on it have done a beautiful job.
I'll make sure these links are available
to some additional resources, sources of information.
Again, if you take away nothing else,
from this talk,
please take a look at the Climate Explorer. Think of it as a resource.
If you have ideas for improvement,
send them in. There's a Contact Us link there.
And also take a look at the national climate assessment.
If you're an engineer,
this is your guidebook of problems that will need to be solved in the future.
So with that, happy to answer any questions.
Thank you so much for your time and interest,
and really a pleasure to be here today.
Thank you. [applause]
Murphy-Wesley: So do we have any questions?
Here's one right here.
Audience member: At one point you said, 34 GCMs,
and at another point you said 30.
[overlap] The slide said 33.
Melton: Sorry, so there's more than 34 GCMs.
At the time we produced this data set
there were 33 GCMs that had completed their data
so we used, I think, 33, and then
We finally went back and added a 34th GCM that had monthly data that we could use.
For the daily scenarios, there were 21 of the global models,
that produced daily scenarios that we could use for the downscaling.
Murphy-Wesley: Do we have any other questions?
Melton: I know there's more than that in the full IPCC setup data of models.
Audience member 2: I'm just curious,
what's the method used to collect the RCP data?
So the RCP data itself, that's developed by teams of economists.
It's-- so the RCP is-- jump back...
The RCPs are done based on a combination of observed greenhouse gas concentrations,
information about energy usage and then some
assumptions that are put together by
economists and social scientists
to try to develop realistic scenarios that --
that represent the potential --
let me go back up here, sorry-- let me get that--
So these are essentially derived from economic and social models
about the potential decisions folks can make.
This is the hardest thing, I think, to represent scientifically
because it's so hard to anticipate
breakthroughs in technology.
The important thing is to keep track as to which scenario we are following.
There's not much divergence. There's still time to change scenarios.
But at the moment, we are tracking this high emissions scenario and have been for some time.
Does that answer your question? Audience member 2: Yeah.
Murphy-Wesley: We have one more question.
Audience member 3: Do you also have data showing that the Earth's mantle's getting warmer
due to increase in temperatures in the Earth?
Melton: Data on-- So yes, so we have-- Audience #3: The mantle, Earth's mantle.
So the --
let me see if I can find a figure here.
You said about the Earth's mass? Audience #3: Yeah, the Earth's core and mantle,
is it getting warmer? And if it is, what are the future problems?
Melton: Right, so again, from all the analysis that's been--
so there is-- mantle and core haven't changed.
One of the things that folks asked about was, what about volcanic emissions?
Emissions of CO2 and aerosols and things like that, so when you have
volcanoes erupt, they do two things.
They emit carbon dioxide and methane,
it's about on average 1% of what humans emit per year.
And then they also emit aerosols.
So they kick up all this dust and all the aerosol emissions.
The aerosols actually reflect sunlight.
And so the net effect of volcanic emissions
is to cool the earth.
You think of Mount Pinatubo. There was this huge eruption that actually resulted in a short-term
cooling of the global surface temperature.
Once those aerosols settle,
then the earth's temperature resume, but the net effect of volcanic emissions
is to cool the earth,
so that's actually something that serves as a small break on the change in temperature
over the last century.
Other questions?
Barbara, there's one more in the front.
Murphy-Wesley: Okay-- oh, one more, okay.
Audience #4: When climate scientists are taking a look at these data models,
did they take into consideration
soil erosion and if those areas are still habitable by humans?
Melton: So soil erosion for the global climate model happens at a spatial scale that's much too
fine, right? That's a really fine scale process.
Part of what we're trying to do with these higher resolution scenarios,
is allow hydrologists and soil scientists
to get accurate realistic projections of future temperature and precipitation conditions
so they can start to model
how these main global drivers might influence local scale hydrology
including changes in runoff
increases in extreme events for runoff,
which have strong influences on erosion.
Other folks are looking at modeling the impacts of
increased temperature and reduced precipitation on
loss of forest ecosystems,
conversion of forest to grasslands,
and how that might affect not only soil hydrology, but also loss of soil cover.
So that--
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