Downscaling Flashcards
What is downscaling?
Downscaling is the general name for a procedure to take information known at large scales to make predictions at local scales.
Why are upscaling and downscaling both error prone?
Some information transmitted and some lost/altered.
Why is it usually unhelpful to impose large grid cell data on small regions unchanged?
This is an extreme example of disaggregation error- values for many locations will be wrong.
How can we go from large scale to small scale?
Two options for downscaling:
Dynamical downscaling
Empirical or statisitcal downscaling
What is dynamical downscaling?
Regional models (RCMs) that run for smaller areas at higher spatial resolution are provided data from GCM simulations – called dynamical downscaling- much finer grid but can do topography much better.
What is empirical or statistical downscaling?
Take underlying heterogeneity (e.g., elevation), calculate the detailed spatial differences and impose on the grid cell (e.g. apply the lapse rate to the surface temperature value) – called empirical or statistical downscaling- if a large climate model just sees something as a single surface you see no variation.
Empirical downscaling
Advantages and disadvantages
Advantages: fine scale
Problem is relationships developed based on today’s observations don’t stay constant.
Regional model downscaling
Advantagesand disadavtanges
Model has scalable grid- can increase resolution
Includes details in topography
Disadvantages: Input variables come from a GCM run (or modern climate data to test the model)- introduces errors.
All models simulate the following:
- Increase in treeline elevation – considerable loss of alpine tundra in mountains
- Loss of shrub-steppe and xeric shrub vegetation in the Intermontane West and its replacement by savanna-steppe or open woodland (e.g. more trees)
?? Higher CO2 increasing water-use efficiency- allows plants to continue to grow in hot conditions- if drought stomata will close and growth is reduced but CO2 helps.
What is the the Uk doing in terms of climate projections?
Uses regional interpolation driven by GCMs and a UK regional model
Basis for estimating likely impacts and for planning, e.g.,
- water supplies
- agriculture
- mortality/ health in the population
- energy demands
- biodiversity management
For a given region (e.g., the South East or all the UK)
i) Projection period (e.g., 2020’s, 2050’s)
ii) Scenario (e.g., a high or a low emissions scenario)
iii) Probability level (e.g., 10%, 50%) – this is a bit tricky.
At 10%…
only 10% of all the used projections fall at or below the value of the variable presented (say, an increase of 2°C).
At 90%…
only 10% of projections fall at or above the value
At 50%…
equal numbers of projections are above and below the value. This does not signify the most likely outcome – but the chances of the temperature change being greater or lesser than (our example, 2°C) are equal.
As well as the climate projections, there is another website that help organisations plan their adaptation to climate change…
UKCIP
This is an extensive site – examples of impacts and solutions and how to plan adaptation