C-LSM
Catchment-based land surface model (C-LSM), actually this is a class of models; several examples exist. In this model plant photosynthesis (and growth) are tightly coupled to transpiration. C-LSM is a model type often used for initialization and forecasting of land surface soil moisture in fully-coupled climate system models. This coupling between photosynthesis and plant growth is critical for seasonal-to-interannual climatological and hydrological prediction.  

EC-Bilt
EC-Bilt is a dynamic atmospheric model that is simple and describes the relevant dynamic and thermodynamic feedback processes to the ocean. As it is computationally ‘cheap’ , it is especially suitable for long simulations (100-1000’s yrs).
More information: http://www.knmi.nl/onderzk/CKO/ecbilt.html

ECHAM5/OM1 model
The ECHAM5/OM1 model is a state-of-the-art climate model, that is used within CcSP and the ESSENCE project (EU project in which KNMI participates). The model is also used for the IPCC 4th assessment report. The model produces a large ensemble of climate runs from 1950-2100. This ensemble allows for analyzing changes in climate extremes, such as rare storms and droughts.
More information:
http://www.mpimet.mpg.de/fileadmin/publikationen/Reports/max_scirep_349.pdf

ECMWF model
Since 1979 the European Centre for Medium-Range Weather Forecasts (ECMWF) develops weather forecasts up to 10 day-projections with use of one of the largest computers of the world. The model is often quoted by weather forecasts on television and newspapers. Less known is that the calculations for weather predictions for a certain periods 30 upto 50 times is repeated, resulting in an collection of climate fore casts (meteorologists prefer to speak about ensembles). Jointly with the standard weather forecasts. The system that generates these ensembles is called EPS (Ensemble Prediction System).
More information: http://www.ecmwf.int/  

MM5/WRF Two versions of another mesoscale meteorological model (like RAMS and RACMO).  

RACMO
Regional Atmospheric Climate Model (RACMO), operational at KNMI, is the Regional Climate model (RCM) used within the CcSP programme. Regional Climate Model (RCM) = a high resolution version of a Global Climate Model (GCM). An RCM is typically used to downscale GCM-results to the regional and local scale, and to address physical phenomena at a greater detail than is possible with course-resolution GCM’s.
More information: http://www.knmi.nl/~roode/RACMO/RACMO.htm  

RAMS
Another regional climate model that is used within CcSP, in addition to RACMO and WRF, is named RAMS (Regional Atmospheric Modeling System). RAMS is a state-of-the-art mesoscale modeling system with applications in atmospheric research, high-resolution weather forecasting, photochemical ozone modeling and precursor transport, air quality studies, acid deposition, long range transport, nuclear emergency response, and environmental and atmospheric research. RAMS can drive advanced Lagrangian particle and Eulerian dispersion models which predict mesoscale pollution impacts in complex, time-dependent, mesoscale circulations. Worldwide there are over 140 sites running RAMS.
More information: http://atmet.com/  

TM5 An atmospheric transport and air chemistry model. It takes the atmospheric flow patterns from other models (like ECMWF) and then computes transport of many trace gas as well as their chemical interactions and interactions with radiation.    

Statistical Models  

Bayseian concepts, Eulerian inversion schemes, SVD inversion
Statistical and mathematical methods used within the mitigation them.  

GLUE (generalized likelihood uncertainty estimation)
A Monte Carlo–based technique that can be used to estimate model parameters, giving precise statistical information about the resulting accuracy/uncertainty of these parameters. GLUE is mostly used for hydrological models. Like METROPOLIS it is limited in applicability because of its heavy computational demands.  

METROPOLIS Is a particular implementation of an Monte Carlo based, Baysian algorithm that can be used to estimate model parameters, giving precise statistical information about the resulting accuracy/uncertainty of these parameters (see also GLUE).