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https://github.com/pacificclimate/ClimDown

PCIC's Daily Climate Downscaling Library
https://github.com/pacificclimate/ClimDown

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PCIC's Daily Climate Downscaling Library

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README 

        
What is ClimDown?

=================



"ClimDown" is a Climate Downscaling package for the `R statistical

programming language`. It was written at the `Pacific Climate Impacts

Consortium`_ (PCIC) with support from `Environment and Climate Change

Canada`_.



The package provides routines for statistical downscaling of coarse

scale global climate model (GCM) output to a fine spatial resolution.



PCIC's suite of routines include several different (yet related)

downscaling techniques. The entire process is named Bias

Correction/Constructed Analogues with Quantile mapping reordering

(BCCAQ) and is composed of the following steps.



* Constructed Analogues (CA)

* Climate Imprint (CI)

* Quantile Delta Mapping (QDM)

* Rerank



See refer to the package documentation for details on each step and

references to the corresponding scientific literature.



  .. _R statistical programming language: http://www.r-project.org/

  .. _Pacific Climate Impacts Consortium: https://pacificclimate.org/

  .. _Environment and Climate Change Canada: http://ec.gc.ca/



Climate Downscaling: What and Why?

==================================



Changes in global climate have widespread impacts on the environment,

economic activity, and human health, especially in high latitudes

where warming is proceeding more rapidly and where ecosystems and

traditional lifestyles are particularly sensitive to the impacts of

warming.



Planning for adapting to climate change requires scientifically sound

information about the future climate. Global climate models (GCMs)

simulate future climate under different emission scenarios. However,

GCMs simulate average conditions over large grid cells--typically on

the order of 10,000 square kilometers or more per cell--which is often

too coarse a resolution for regional and local applications. The use

of original GCM data is not always the best option to provide

adaptation-relevant information at the local scale.



Bias in model simulated local climate is of concern for many

applications. For example, compared with observations, the median

temperature simulated by GCMs from the Coupled Model Intercomparison

Project Phase 5 (CMIP5) shows biases relative to Climate Research Unit

high-resolution gridded dataset (`CRU TS3.10`_) ranging from -3° C to

1.5° C for seasonal and annual mean temperatures in 26 global land

areas (`Flato et al. 2013`_).  Precipitation simulated by CMIP5 models

is also biased relative to observations (`Flato et al. 2013`_). These

biases hinder the direct application of model simulated future climate

for impacts modelling and adaptation planning since climate impacts

are often related to certain physical or biophysical thresholds. As a

result, adaptation planning often uses model simulated future climate

information that has incorporated some sort of downscaling and bias

correction. Additionally, climate information is more useable and is

less prone to misinterpretation when presented in a manner specific to

the local community and/or impacts most relevant to a particular

sector. High-resolution future projections of impact-relevant climate

indices can be particularly useful in this regard.



ClimDown has been used to produce such bias corrected, downscaled GCMs

for current and future climates, and could be used to do so for

anywhere else in the world.



.. _Flato et al. 2013: http://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_Chapter09_FINAL.pdf

.. _CRU TS3.10: http://dx.doi.org/10.1002/joc.3711



Installation

============



You can install the latest `ClimDown release from CRAN`_ using the R

interpreter: ``> install.packages('climdex.pcic')``



.. _ClimDown release from CRAN: http://cran.r-project.org/web/packages/ClimDown/index.html



If you are interested in a development version or a specific release

of ClimDown, you can use the `devtools` package as an installation

alternative.::



    > install.packages('devtools')

    > devtools::install_github("pacificclimate/ClimDown", ref="release")

    # Or

    > devtools::install_github("pacificclimate/ClimDown", ref="1.0.1")



System dependencies

-------------------



ClimDown reads all of its input and produces its output in `NetCDF

format`_ and manages numeric units using the `UDUNITS2 library`_. The

NetCDF and udunits2 libraries are system dependency of the

package. Ensure that the following packages are installed under

Debian/Ubuntu Linux systems: libnetcdf-dev, netcdf-bin, and

libudunits2-dev. For other systems, follow the installation `NetCDF

install instructions`_ and `UDUNITS2 install instructions`_ provided

by `Unidata`_.



.. _NetCDF format: https://www.unidata.ucar.edu/software/netcdf/docs/netcdf_introduction.html

.. _UDUNITS2 library: https://www.unidata.ucar.edu/software/udunits/udunits-current/doc/udunits/udunits2.html

.. _NetCDF install instructions: https://www.unidata.ucar.edu/software/netcdf/docs/getting_and_building_netcdf.html

.. _UDUNITS2 install instructions: https://www.unidata.ucar.edu/software/udunits/udunits-current/doc/udunits/udunits2.html#Installation

.. _Unidata: https://www.unidata.ucar.edu/



Necessary Resources, Performance, and Platform

==============================================



The BCCAQ algorithm implemented by ClimDown is a complex, multi-stage

operation, and as such performance will vary widely depending on the

size of the input, the degree of parallelism selected by the user, and

the performance characteristics of user's system (CPU speed, available

RAM, I/O speed).



We have `previously written`_ about some of the `complexities involved

in downscaling performance`_, but it remains an area of active study.



Consider our experience as a matter of anecdote. We typically run

ClimDown for downscaling 150 year, daily GCM simulations to a

Canada-wide ANUSPLIN grid (approximately 10km resolution, 1068 by 510

cells). On our Linux systems, such runs can take up to 7 days to

complete. However each of the different downscaling steps has

different opportunities for parallelism and different performance

characteristics. Typical of our runs is something like this:



* CI: 1 core, 10 GB RAM, Run time ~ 7 hours

* CA: 8 cores, 10 GB RAM, Run time ~ 2 hours

* QDM: 1 core, 36 GB RAM, Run time ~ 1.5 days

* rerank: 4 cores, 8 GB RAM, Run time ~ 4 days



In general, this downscaling technique is very expensive for large

spatiotemporal domains. The more you can limit your domain, the faster

your runtime will be. For small domains, it may be possible to run

ClimDown on a typical workstation, but in general we do all of our

production runs on rack-mounted supercomputers.



Though Windows binaries for ClimDown are available `from CRAN`_, no

effort has been made to optimize this package for Windows and your

mileage may vary.



.. _previously written: http://james.hiebert.name/blog/work/2016/04/26/BCCA.html

.. _complexities involved in downscaling performance: https://github.com/pacificclimate/ClimDown/blob/doc/doc/report.md#rewriting-numerous-algorithms

.. _from CRAN: https://cran.r-project.org/web/packages/ClimDown/index.html