Depends on your needs, but you can try this one:

http://cran.r-project.org/web/packages/qmap/index.html

Thanks a lot. I've just started Principles of Planetary Climate by Dr. Pierrehumbert. I'll try the software that you recommend me...

do you want downscale by statistical or dynamical methods? for statistical down scaling methods, ASD (automated statistical downscaling) and SDSM (statistical downscaling model) model are the best. you can free download in internet.

Thanks Baguer for this information, I am interested in dynamical methods. @Kampmann and @Gudmundsson the links are very useful. Thanks.

Hi all,

FYI the above paper can be downloaded here:

http://eprints.uni-kiel.de/11662/1/2009RG000314.pdf

Regards,

Tito

hello sir,

can you suggest me any paper related to climate modelling on the basis of vegetation

Rafael,

To downscale precipitation, here are 2 of the many more approaches you can take.

1) Use a statistical technique to go from larger-scale GCM or RCM-simulated precipitation to finer spatial scale. One such technique is quantile mapping (aka BCSD, Bias correction and statistical disaggregation), as recommended here by Lukas Gudmundsson. If you go this route, the work is already done for you worldwide and you can download it (precip and temperature) from here: http://www.climatewizard.org/

2) If extremes of precipitation are of interest to you, however, there may be better approaches. You can use co-variates (such as GCM or RCM-simulate sea level pressure, 850hPa-atmospheric humidity and temperature) and study how your observed precipitation extremes co-vary with those. You can then develop a statistical model of precip extremes based on the co-variates (e.g. you can fit a GEV to the precip whose parameters are functions of the co-variates). You can train this model based on observations of precip and reanalysis of the co-variates, then apply the model to the future projections.

This is not an easy topic to describe in this forum, but there are lots of publications about (1) and about (2). (1) is an easy route. (2) will require you to do quite a bit of research and (judicious) data studies.

There are other approaches as well. You can even use plausible hypothetical scenarios of how the precipitation intensity-duration-frequency curves might change in the future, e.g. as a function of temperature, higher moisture content, and projected changes in storm trajectories..

Mariza

Hi,

Could I downscaling/get numeric data from MAGICC/SCENGEN result ?

plz sir anybody can send me link for ASD tool, bcoz i wont able to find it.ill be very thankful.

A very simple downscaling approach is statistical downscaling SDSM. There are lot of research and paper related to them. You can also search for other downscaling methods such as WRF, PRECIS and many more. 

There is no single best way, it depends on the goal. Dynamical downscaling with regional climate models have been demonstrated to have added value for downscaling global models. They are physically based and their higher resolution allows to better represent local circulations, orographic effects, convection, etc. However they inherit the uncertainties from the large scale and add their own, so often ensembles of rcm simulations are required to get a picture of the uncertainty and plausible outcomes. This us computationally very expensive and a huge workload. Yet, projects like CORDEX provide databases of such simulations over different domains which you can explore.

There are also mant statistacal downscaling methods, show to provide good results. Typically they require a good calibration dataset which is often hard to obtain and their extension do a different climate is often problematic

For hydrological purposes one may often use land-surface models to downscale different land water variables. It is still physically based and much cheaper than a full rcm, but it requires to assume that global atmospheric fields provide a good forcing and does not allow for land-atmosphere feedbacks

Statistical downscaling is recommended to study impact at local condition. Many researches have already explored the superiority rather than Dynamic downscaling. Any tool can be used and it can develop as well for a particular local condition.

https://doi.org/10.1016/j.agwat.2019.01.002

Article Modeling climate-smart decision support system (CSDSS) for a...

I recommend ASD and SDSM model. be couse they are simple and free. you can find in internet.

Hi,

You can use the climate perturbation tool to do that. Please check the attachment if you are interested.

Can I know which software can be used for this downscaling

Statistical downscaling such as (SDSM) if you want easier option.

This paper on "Representative general circulation models selection and downscaling of climate data" might be helpful:

https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.6447

Hi, I recommend SDSM or ASD model for downscaling

I would recommend the SDSM model (https://sdsm.org.uk/sdsmmain.html). We have been using it for a while in our studies :

Article Basin-wide hydrological system assessment under climate chan...

SDSM model is a good choice, but you need to bias correct the results. Here is the paper that assesses the performance of SDSM and different bias correction methods.

Article An Uncertainty-Based Regional Comparative Analysis on the Pe...

Dear Rafael

We have worked with the information already downscaled on the site:

http://www.ccafs-climate.org/data/

There you will find information on climate change scenarios at a spatial resolution of 1 km, which is sufficient for many applications. So if 1 km is enough for you, your downscaling is done. If you need a higher spatial resolution, for example 250 m or 90 m, you need to do the downscaling with some software. For example, you could check the following website:

https://leonawicz.github.io/Downscaling/ds_minmax_temps.html

You could check out these articles:

Navarro Racines, Carlos Eduardo; Tarapues Montenegro, Jaime Eduardo; Thornton, Philip; Jarvis, Andy; Ramirez Villegas, Julian (2019). CCAFS-CMIP5 Delta Method Downscaling for monthly averages and bioclimatic indices of four RCPs. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/CCAFS-CMIP5_downscaling

Bedia, J., Baño-Medina, J., Legasa, M. N., Iturbide, M., Manzanas, R., Herrera, S., Casanueva, A., San-Martín, D., Cofiño, A. S., and Gutiérrez, J. M.: Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment, Geosci. Model Dev., 13, 1711–1735, https://doi.org/10.5194/gmd-13-1711-2020, 2020.

We have generated several scripts in R to do downscaling of climate data and we have reached a spatial resolution of 90 m in some work done in Chile. If you wish, we can talk sometime, you can look for my email with my name in my university.

Best regards