Hello and welcome to the Minnesota Department of Health’s webinar on the basics of climate
projection data.
I’m Brenda Hoppe, a research scientist with the Minnesota Department of Health’s Climate
& Health Program.
For nearly a decade our program has been studying and addressing climate change impacts on human
health and well-being, long enough that we’ve seen a lot of progress in this area.
Helping to fuel this progress are the great advancements that have occurred around climate
projection data.
This presentation was originally shared at the first meeting of the Minnesota Climate
Data Community of Practice.
This Community of Practice is an open, active group intended to help steward the use of
climate information for advancing resiliency planning throughout Minnesota’s various
state and local agencies.
You can learn more about this community of practice as well as other activities led by
the MN Climate & Health Program at our website, listed here.
The goal of this webinar is to provide data users in Minnesota with a brief introduction
to climate projection data, but first I’d like you to keep in mind the broader context
for this powerful source of information.
We are at a pivotal moment in the growth of our understanding on climate change, specifically
with regard to climate and computer science, which are offering us, through the provisioning
of climate projection data, a dramatic shortcut to climate resiliency.
Use of climate projection data in analysis and planning will not only help us prepare
for the harsh realities of climate change but will provide the foundation for cross-cutting
innovations and truly inventive policies that otherwise may have been slow to evolve without
the pressure of climate change.
As an environmental public health scientist, I think of this point in our history and the
potential impact of climate projection data, much like the moment in the mid-19th century
when Louis Pasteur discovered that organisms invisible to the human eye can cause fatal
diseases.
Pasteur launched the field of microbiology and with it came a burst of microbial data
that allowed us to identify and prevent the transmission of bacteria through our water,
air, and food that were the leading killers of the era.
Microbiology gave us data that allowed us to see and address hidden threats in our environment.
Climate projection data carry that same potential, the potential to see into places that were
previously hidden to us, this time that hidden place is the future.
Simply put, climate projection data allow us to see and address the hidden threats of
climate changes that may occur in our future.
So what are climate projection data and why are these datasets such a big deal?
In a nutshell, climate projection data are estimates of future temperature and precipitation
measures or other climate characteristics, like solar radiation or wind speed, that are
derived from very sophisticated global climate models for time periods many decades into
the future.
Not only do these models capture the fundamental mechanisms of our planet related to physics,
chemistry and biology, but they also incorporate a range of possible greenhouse gas emissions
scenarios, potential technology or population changes, and their influence on all these
natural mechanisms.
Without a crystal ball, model simulation of our future climate is the only method that
allows us to study how the climate may respond to increasing levels of greenhouse gases and
their disruption on weather patterns and ecosystem responses.
Let’s unpack a bit how these models work and what’s inside of them.
Climate models, also known as general circulation models or GCMs, use highly detailed mathematical
equations to characterize how energy and matter interact in different parts of the ocean,
atmosphere, and land.
Building and running a climate model is a very complex process of identifying and quantifying
Earth system processes, representing them with mathematical equations, setting variables
to represent initial conditions and subsequent changes, and repeatedly solving these equations
using powerful supercomputers across time periods that stretch out to the end of the
century.
Climate models separate Earth’s surface into a 3-dimensional grid of cells that can
interact with each other.
The results of processes modeled for one cell is passed on to neighboring cells in order
to model the exchange of matter and energy over time and over the earth’s entire surface.
Generally speaking, grid cell size is what defines the resolution or detail of the climate
model: in other words, the smaller the size of the grid cell, the higher the level of
detail in the model.
Once a climate model is set up, it can be tested through a process called “hind-casting”
or backtesting.
Hind casting involves running the model from the present backwards into the past.
Model results are then compared with what was actually observed to see how accurately
the model estimated those real world observations.
Science teams around the world are involved in building and testing climate models, comparing
their model outputs to actual observations and also results from each other’s’ models.
Once a climate model is proven to perform well in hind-casting tests.
In other words once it has shown it can represent past phenomena adequately, then it’s considered
valid for projecting what might occur in the future.
And this is where things get interesting because once you’ve designed a model that functions
accurately, you can then tweak parts of that model or add new inputs to see how these disruptions
or additions can impact the results.
With regard to the influence of rising greenhouse gas emissions, you can add and change factors
related to these emissions and project how they may alter key climate variables, like
temperature, precipitation, wind speed, solar radiation or cloud formation.
Basically, you can start to create scenarios based on potential future levels of greenhouse
gas emissions.
Let’s talk in a bit more detail about emissions scenarios because climate projection data
are often packaged according to these scenarios.
As I mentioned, there are a number of research groups around the world that develop climate
models.
To ensure that the results from these models are complementary and comparable, a standard
set of GHG emissions scenarios are used to ensure that starting conditions, historical
data and projections are used consistently across models.
You can think of scenarios as packages of information that reflect the major factors
that both influence and are influenced by greenhouse gas emissions.
Back in 2000, the world’s leading authority on climate change, the Intergovernmental Panel
on Climate Change or IPCC, issued its Special Report on Emissions Scenarios which described
4 scenarios that captured a range of possible climate futures.
These were commonly referred to by letter-number combinations such as A1, A2, B1, and B2.
The SRES scenarios have been in use for more than a decade, and it’s likely that you
may have seen them referenced in reports or studies.
In simple terms, the four scenarios represent divergent paths in the predominant drivers
of climate emissions: economy, environment, globalization and regionalization and are
characterized with data representing population changes, economic growth, technological advances,
energy use, land use, and agriculture.
So for example, the A2 scenario, and the data that populate it, represent a future world
characterized by increasing global population and technology advances that are fragmented
and slow.
You can see how this scenario correlates to higher emissions and a major increase in projected
global temperatures over time.
Conversely, the B1 scenario represents a world where global population peaks mid-century
and then declines, with rapid changes toward a service and information economy, reductions
in material consumption, and widespread adoption of clean technologies.
These characteristics are linked to reductions in emissions and thus a more dampened increase
in projected temperatures.
However, as I mentioned climate projections are in a rapid state of advancement and in
2013, climate scientists agreed upon a new set of scenarios that are modeled a little
differently than the old SRES scenarios.
These new scenarios are known as Representative Concentration Pathways or RCPs and they’ve
replaced the old SRES scenarios as the gold standard for climate data selection.
There are four RCP pathways: RCP8.5, 6, 4.5 and 2.6.
The numbers correspond to a range of radiative forcing represented as watts per square meter.
Radiative forcing is a measure of the influence that emissions have on the earth’s energy
balance.
The rate and trajectory of the forcing is considered the pathway.
Just like the previous SRES scenarios, RCPs likewise are based on different assumptions
about population, economic growth, energy consumption and land use over the century.
However, the RCPs represent a wider set of futures than the previous SRES scenarios and
now explicitly include the effect of mitigation strategies.
For example, RCP 2.6 represents a very ambitious mitigation scenario with emissions peaking
around 2020 and then rapidly declining.
It assumes that global population peaks mid-century and that oil use declines and use of other
fossil fuels is offset by widespread adoption of technologies for removing CO2 from the
atmosphere.
You can see how these factors lead to lower CO2 levels and a lower rise in projected global
temperatures.
Conversely RCP8.5 represents a sort of “worst case scenario” future in which emissions
continue to increase rapidly, population growth stays high while incomes and per capita growth
in developing countries stay low.
This scenario is highly energy intensive assuming oil and coal use continues to grow rapidly.
Land use continues on current trends with crop areas increasing and forestland decreasing.
Here you can see how these factors lead to higher CO2 levels and a much greater increase
in projected global temperatures over the century.
Now I’d like to just briefly touch on a methodology that is central to the rapid advancement
of climate projection data for state and local decision-making.
And that methodology is referred to as downscaling.
Downscaling output from global climate models involves incorporating local data and local
models to increase the resolution or detail of the original global climate model output.
Downscaling really is a game changer because by increasing the granularity of climate data,
improving how well the output from global models represents climate locally, now makes
these data applicable and useful for planning.
There are two main approaches to downscaling --statistical and dynamical.
Dynamical downscaling relies on the use of regional climate models or RCMs driven by
GCM output to produce highly regionalized climate information.
In contrast, statistical downscaling uses statistical relationships to predict regional
climate variables from large-scale variables.
Each downscaling method has its strengths and weaknesses.
Statistical downscaling is less computationally demanding and can be more easily applied,
but it requires long‐term, high‐quality surface observations to establish a robust
statistical relationship between large‐scale variables and local variables.
Dynamical downscaling involves the use of regional models that characterize highly detailed
physical processes in a local area, and therefore can be much more highly resolved but is also
more computationally expensive.
Regardless of the approach, downscaled climate projection data are essential tools we need
to inform effective resiliency planning.
Our program gained some experience with climate projection data while conducting a vulnerability
assessment on private wells.
For this assessment the Climate & Health Program worked closely with the MN State Climatology
Office to characterize the potential for increased nitrate contamination pressures on MN private
wells from flooding linked to changes in heavy rainfall events.
Application of precipitation projection data was central to this project.
Briefly, for this vulnerability assessment we combined precipitation projection data
from the USGS National Climate Change Viewer for MN counties, with data representing determinants
of population vulnerability, such as exposure and sensitivity, to derive a composite vulnerability
index, all in a GIS platform.
This index allowed for easy mapping of areas in the state where nitrate contamination from
flooding may put certain susceptible populations, particularly infants, at risk for health outcomes
like methemoglobinemia from future increases in heavy rain events.
There were a number of important lessons we learned about climate projection data during
this study, which we outline in our paper, but above all the experience helped us realize
the fundamental necessity of these data for crafting smart climate resiliency strategies.
Already there are a number of nations, even a few US states, that have partnered with
expert modelers to obtain downscaled data specific to their regions and they are using
these data across numerous sectors to advance innovative resiliency work for their citizens.
For example, in 2016 Massachusetts’s Governor signed an executive order that enabled funding
for modelers with the NE Climate Adaptation Science Center to develop downscaled projections
for future changes in temperatures, precipitation and sea level rise specific to Massachusetts.
These data are publicly available through the resilient MA data portal.
California has Cal-Adapt a similar online geospatial data tool where anyone can access
localized climate change projections for the state produced by California’s own scientific
and research community.
Cal-Adapt is currently the gold standard for the provision of climate projection data.
An end-user can get projections related to temperature, precipitation and sea level rise,
as well as snowpack, drought, wildfire, and stream flows.
So how about Minnesota and our own quest to obtain climate projection data downscaled
for our state?
In fact there are a number of groups and individuals from both the private and public sector that
have spent years trying to find the policy and funding levers to enable development,
access and practical use of climate projection data for advancing resiliency strategies in
Minnesota.
Groups like the MN Resiliency Collaborative continue to advocate for these data but until
Minnesota joins the ranks of Mass and CA, we can still grow our knowledge base in this
area and help one another utilize what data are available through the MN Climate Data
Community of Practice.
The MN Climate & Health Program convened this CoP to: connect diverse data users with climate
scientists working in Minnesota; to share insights for obtaining, preparing and applying
climate projection data in various disciplines; brainstorm solutions for overcoming challenges;
establish consensus over use and interpretation of these data; and, ultimately to further
cross-agency, cross-disciplinary collaborations in Minnesota around climate resiliency.
The MN Climate Data Community of Practice is open to all but particularly to state and
local agency data users.
For information on the next CoP meeting or to learn about other projects and activities
with the MN Climate and Health Program, please sign up for our newsletter on our website.
And with that I thank you so much for your time and interest in climate projection data,
encourage you to stay in touch, and wish you a very wonderful day.