Transpose AMIP



Experimental design

Data requirements

Call for diagnostic subprojects

Accessing data



Contact / Steering Committee


Diagnostic analysis sub-projects

Widespread use of the Transpose-AMIP II data is encouraged. The data are freely available through the Earth system Grid federation for research purposes - see Accessing data for details. Conditions on the submission of data from some centres mean that transpose-AMIP II data cannot be used for commercial purposes.

A call for diagnostic analysis subprojects has been launched so that the community can see what work is proposed and to act at a catalyst for further subprojects. If you are interested in working with the transpose-AMIP II data, please send an email
to with the name of the sub-project leader and paragraph outlining what you are planning to do. Submitted sub-project summaries will appear below.

Proposed subprojects

Relationship between short and long timescale model errors (PI: Shaocheng Xie)
Comparison of the composite biases in the short-range forecasts to the long-term mean climate biases. This will utilise the alignment of Transpose-AMIP with CMIP5 to investigate which climate model biases develop on short timescales and may be investigated using the Transpose-AMIP approach (and how much commonality there is in these biases between GCMs), and which develop on longer timescales.

Intense extratropical windstorms (PI: Peter Knippertz)
This project is funded by the AXA Research Fund and will apply seamless model evaluation approaches to historic devastating wind storms over the North Atlantic and Europe. 
We will assess the capability of climate models submitted to transpose-AMIP in spinning up intense cyclones when given observed initial conditions relative to higher-resolution NWP models with more complex physics. This will unveil the role of fast processes in uncertainties of future projections of such storms. In addition we plan to run some of the CMIP5 and other models in similar experiments on additional storms not covered in transpose-AMIP to build sufficient statistics.

Regional investigation into model tendencies (PI: TBD)
This sub-project aims to compare the model forecasts at different lead times against analyses and observations. Different key regions will be analysed to investigate which regions have the largest error growth and how that effects the subsequent evolution of the forecast. The model tendency diagnostics will be used to associate these biases to areas of the model science. The principal timescales for development of biases and the comonality of biases between GCMs will be explored

Comparison of current climate and NWP models (PI: TBD)
Climate models submitted to transpose-AMIP will be compared with current NWP models. Analysis will include standard NWP and climate metrics together with more process-based composites. The sub-project will explore the role of resolution verses any differences in the physical formulation of climate and NWP models and identify common strengths and weaknesses.

VOCALS analysis (PI: Thomas Toniazzo)
16 of the transpose-AMIP hindcasts lie within the period of the VOCALS fields campaign. Stubborn SST, wind and surface evaoporation errors in coupled GCM integrations  have been shown to persist irrespective of oceanic or atmospheric resolution in  the tropical marine Sc areas of the Southern Hemisphere, particularly the Humboldt  and the Benguela upwelling sustems. Such errors appear to be qualitatively identical  in all of the CMIP5 integrations that have been made available up to this time.  Similar surface wind and evaoporation errors, in particular, are also found in AMIP  integrations of the same models. Initial tendency analysis (ITA) performed on  initialised integrations for the GloSea4 (based on HadGEM2), HiGAM, and CanCM4  (at CCCMA) models, show that coupled atmospheric surface-wind and precipitation  errors are the first to appear, within day one of the integrations, with SST  responding over a period of one to three months. It is therefore believe that these  wind errors represent the initial trigger for the coupled errors in the GCS.  Systematic ITA over the first week of hindcast integrations of different models  with homogeneous initialisation therefore promises to shed light on the relationship  between large-scale circulation errors and the errors in the radiatively important  areas of tropical stratocumulus. This subproject involves case study comparison of the simulation of stratocumulus over the SE Pacific with field data collected during the campaign.

2009 SE Asian monsoon analysis (PI: TBD)
The 2009 SE Asian monsoon was notable for being unusually dry. This was primarily due to the late onset in June, although rainfall was also lower than normal during July and August. 16 of the transpose-AMIP hindcasts lie within July and August 2009, so this project aims to test the ability of the models to simulate SE Asian rainfall at different forecast lead times in terms of its intensity and change between active and break periods.  

MJO dynamics in the Transpose-AMIP II hindcasts:  (PI: Mitch Moncrieff) 
The YOTC-ECMWF 2-year database (May 2008-April 2010) consisting of high-resolution analysis, forecasts and special diagnostics, samples a complete ENSO cycle.   La Nina conditions during the first year associated with weak short-lived MJOs was followed by El Nino conditions and two strong (successive)  MJOs. The Asian-Australian summer monsoons during the “Year” displayed a comparable level of variability: the normal monsoon rainfall of 2008 contrasted with the relatively dry 2009. Both years display intraseasonal variability of rainfall possibly due to the MJO and/or the northward propagation of the ITCZ in the Indian Ocean. The occurrence of strong wintertime MJOs in El Nino conditions, the shorter and weaker MJOs in La Nina conditions raises the following questions: What controls  the amplitude of the MJO, is organized convection an important factor ? Do the dynamics of summertime MJOs in the Indian Ocean differ from that of wintertime MJOs in the western Pacific? Are weak MJOs more difficult to predict than strong ones?  If so, does the MJO amplitude depend upon the dynamics of the embedded organized convection?  
Three weak MJOs identified for numerical experimentation and analysis in the YOTC Implementation Plan (see occurred within the periods selected for the 64 hindcasts of the Transpose-AMIP II project: i) the mid-October 2008 MJO followed a suppressed phase subsequent to the mid-August  MJO; ii)  the late January  2009 MJO which propagated into northern Australia was associated with Queensland floods,  tropical cyclones, and wildfires in the Melbourne region; iii) the April-May 2009 MJO, the strongest in the “Year” up to  that time,  involved convectively coupled atmospheric Kelvin waves and may been a factor in the transition to El Nino. 
The above questions will be addressed in the context of the MJO hindcasts for the October 2008, January 2009, and April-May 2009 MJO events, assisted by the Goddard Giovanni satellite analysis system developed for the YOTC project (YOTC-GS).   Nested regional climate model (NRCM) simulations of one of the MJO events, forced at the outer lateral boundary by YOTC-ECMWF 25 km and 15 km global analyses, may be conducted depending upon the results of the hindcast analysis. 

Water budget analysis (PI: Gill Martin)
The analysis of regional water budgets can provide information on the role of moisture transports and local processes in the development of model errors on short to seasonal timescales and beyond. Transpose-AMIP provides an opportunity to compare the development of water cycle errors in several current GCMs. Regional water budget analysis will be carried out over several key regions and the results compared between models to see over what timescales model errors develop in the different regions and where similarities and differences occur between models.

Comparison of methodologies (initial tendency using own analysis vs 5-day forecast using alien analysis) (PI: Mark Rodwell)
Work at ECMWF will involve a comparison of ‘Initial Tendencies’ and ‘Transpose-AMIP’ (forecast bias out to D+5) methodologies for assessing a model’s representation of the physics (and dynamics).
Initially, we will repeat the experiments of Rodwell and Palmer (2007) with a (possibly less extreme but nevertheless erroneous) perturbation to the convective entrainment parameter (other choices left unchanged: e.g., resolution T159). We will document and attempt to explain the evolution of the impact of such a perturbation (relative to the control model) with initial tendencies and, over subsequent forecast lead-times, as the impact is ‘felt’ further-a-field such as in the strato-cumulus regions, or in the surrounding regions where strato-cumulus ‘transitions-to’ cumulus. The same perturbed model will also be initialized from a ‘non-native’ set of analyses (as in the Transpose-AMIP methodology, but produced here using the control model) to investigate the importance of using a native analysis. One could also delve further into the data assimilation system and diagnose, for example, the variational bias corrections applied to observations within some key cloud regions. The use of additional observations (e.g. CloudSat) could also be considered. Ultimately, the two main questions to answer are (1) ‘How do errors (in, e.g. strato-cumulus cloud and their radiative effects) develop over the course of a forecast?’ and (2) ‘Which experimental designs and diagnostics (initial tendencies, D+1, …, D+5 error, atmospheric model climate) successfully identify the model error?’

Cloud regimes (PI: Keith Williams)
Follow an analysis methodology similar to Williams and Brooks (2008, J. Climate) to look at the development of errors in cloud regimes (derived from clustering ISCCP simulator data), and relate these to climatological errors in the AMIP simulation.  Other cloud observations (CloudSat, CALIPSO, etc.) with the associated simulator output from the models, will be used to gain a more complete understanding of the development of biases in the regimes.