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Downscaling of climate model projections

Technology group:


Downscaling of climate change models is the procedure of using large-scale climate models to make
climate predictions at finer temporal and spatial scales to fit the purpose of local level analysis and
planning. This typically involves use of Global Climate Models (GCMs) representing physical processes in
the atmosphere, ocean, cryosphere and land surface, simulating the response of the global climate
system to increasing greenhouse gas concentrations, using different emissions scenarios. Downscaling
tools such as high resolution regional climate models (RCMs) can then be used to simulate the scenarios
on a finer spatial scale, using finer level local conditions.

There are two general approaches of downscaling:
Dynamical – were outputs from GCM’s are used to drive higher resolution regional climate models with
a better representation of local terrain and other conditions
Statistical – where statistical links are established between large-scale climate phenomena and observed
local-scale climate. (These are always needed to correct for biases even in RCM’s).


Downscaling methodologies can take many forms, but usually starts with selecting the appropriate
downscaling approach (statistical or dynamical). This is followed by preparation of input datasets - local
and global, and further a simulation of large scale variables (GCMs) to local circumstances. The results of
these can then be coupled with hydrological modelling tools as appropriate to determine local impacts
on water resources – e.g. for vulnerability assessments of water resources. They can also be fed into
various adaptation scenario development.

Environmental Benefits

- Scenario outputs quantifying climate impacts assists in planning for adaptation responses that are
sustainable and appropriate for future climate conditions in the given location
- Helps to identify most vulnerable ecosystems for intervention

Socioeconomic Benefits

- Improved preparedness for future climate conditions, including extreme events such as floods and
droughts helps to minimize losses and damages resulting from improper adaptation
Opportunities and Barriers
- Improved understanding of local future climate conditions and their impacts on water
- Continuously improving technologies and methods


- High levels of uncertainty and numerous scenarios with various, often highly different
outcomes can make it difficult to derive conclusive results
- Computationally and resource demanding
- The uncertainty of results increases as the projections are downscaled for local applications
(higher resolution).

Implementation considerations*

Technological maturity: 3-4
Initial investment: 2-3
Operational costs: 2
Implementation timeframe: 1-3

* This adaptation technology brief includes a general assessment of four dimensions relating to implementation of the
technology. It represents an indicative assessment scale of 1-5 as follows:
Technological maturity: 1 - in early stages of research and development, to 5 – fully mature and widely used
Initial investment: 1 – very low cost, to 5 – very high cost investment needed to implement technology
Operational costs: 1 – very low/no cost, to 5 – very high costs of operation and maintenance
Implementation timeframe: 1 – very quick to implement and reach desired capacity, to 5 – significant time investments needed
to establish and/or reach full capacity
This assessment is to be used as an indication only and is to be seen as relative to the other technologies included in this guide.
More specific costs and timelines are to be identified as relevant for the specific technology and geography.


UNEP-DHI Partnership: Down-scaling of Climate Model Projections