(chiming musical introduction)
>>Hello, I'm Dr. Carlos Gaitan,
research scientist at South Central Climate Science Center
of the University of Oklahoma.
I work in Princeton, New Jersey,
at the Geophysical Fluid Dynamics Laboratory
of the National Atmospheric and Oceanic Administration.
In this short video,
I will introduce the concept of statistical downscaling.
As decision makers across the globe
have started to struggle with climate change,
they have a need to quantitatively assess potential impacts.
One major stumbling block in these pursuits
in the transition of information
provided by global climate models,
or GCMs, to local impacts.
The process of downscaling
is typically used to breach this gap.
Statistical downscaling techniques
are divided in three general categories.
Regression models, weather generators,
and weather typing, or classification schemes.
Roughly speaking,
statistical downscaling techniques build relationships
between graded observational-based
time series or in situ observations directly.
And the largest scale predictors are
resolution output from numerical models like the GCM's.
Regression techniques represent
linear or non-linear relationships
between the local scale variables, often called targets,
and the large scale predictors.
As an example, let's assume that
we want to downscale temperature over northern Oklahoma
using simple linear regression.
The first step is to obtain sufficient
historical observations from a weather station
or a observational product for the area.
Then we will accept physically relevant
predictors from historical re-analysis products
that are synced in time with our observations.
We then can fit our regression equation
between our target and the predictor or predictors.
After obtaining the base equation
we can calculate the error and correlation
between the downscale time series
and the target giving us an understanding
of how well this equation represents historical record.
Finally we use this equation and similar set
of predictors obtained from the GCM
to obtain the historical conditions
and future projections downscaled to Norman, Oklahoma.
As we do the downscaling techniques
it is important for us to obtain a set of predictors
that adequately represent the relationship
between the large scale atmosphere
and the local scale variable of interest.
This choice is particularly important
in your mountain and coastal regions
where there can be large variations in model performance.
Still, regression techniques have been popular
because they are easy to implement.
The second technique a weather generator
attributes of a local climate variable
such as it's mean and standard deviation.
It does not replicate the observed sequences
of events.
A simple weather generator can
relay the distribution of a variable
from the global climate model output
to the distribution of the local scale variable of interest.
For example, quantum mapping techniques
may link or map the observations
and historical GCM output in intervals
with the core probabilities or quantiles.
These mapped relationships then
are used to generate downscale projections
using the GCM output for future times.
The last technique, weather typing or classification,
use analogs or past atmospheric patterns.
In this case we select a dates or dates
in the historical period when the large scale
conditions most closely resemble the day
for which the projection is made.
Imagine that you have a collection of
weather maps for the south central United States
from many different years.
Some weather maps can be classified as dry days,
some as wet days, some as higher than normal temperatures
or lower than normal temperatures.
Now let's retrieve the observations
from the weather station at Norman
that we used in the previous example.
We can now link each individual weather map
to corresponding observations from our weather station.
Once we determine the statistical link
we compare every single historical map
with a future map obtained from the
GCM future projections to find the best match.
After we select the historical map that
most closely resembles the future map
we assign the corresponding value from
the weather station data as our
downscale projection for that time period.
We repeat the process until all
the future maps are classified,
and we generate a full time series
of downscale data.
This technique is useful if our future maps
look like our historical maps,
but if the climate changes too much
and the future maps are different than historical ones
then this technique might not be as useful.
Overall, it's important to keep in mind
that all the statistical methods rely
on several assumptions.
The main assumption is the relationships
between predictors and targets
do not change over time.
This assumption is typically called stationarity.
Because we know that our climate is changing
the stationary assumption limits the
effectiveness of statistic downscaling.
We also assume that the predictors and the target
are linked physically by the climate system
that the predictors from the GCM carry
the climate change signal and that it models
correctly simulate the predictors use for downscaling.
In practice these assumptions should
be tested during the historical period
by comparing the GCM output with different products
and local observations.
Although no statistically downscaled
data set is perfect decision makers can use
a group of these data sets to determine
the range of possible climates
that day might face in the future.