At the Tyndall Centre, University of East Anglia, Rachel Warren has led the development of the Community Integrated Assessment System (CIAS). CIAS brings together the academic research community’s disparate numerical models and climate-related datasets into a common framework (Warren et al 2008).

As such it is a unique multi-institutional modular and flexible integrated assessment system for modeling climate change and sustainable development.  Key to its development is the supporting software infrastructure, SoftIAM.  Through it, CIAS is distributed between the communities of institutions which have each contributed modules to the CIAS system.

At the heart of SoftIAM is the Bespoke Framework Generator (BFG) developed at the Centre for Novel Computing, University of Manchester, which enables flexibility in the assembly and composition of individual modules from a pool to form coupled models within CIAS, and flexibility in their deployment onto the available software and hardware resources.

Such flexibility greatly enhances modelers’ ability to re-configure the CIAS coupled models to answer different questions, thus tracking evolving policy and adaptation needs.  It also allows rigorous testing of the robustness of IA modeling results to the use of different component modules representing the same processes (for example, the economy).  Such processes are often modeled in very different ways, using different paradigms, at the participating institutions.

In CIAS, data describe the global economic system, greenhouse gas emissions, the earth system and its climate, the potential impacts of climate change upon human and natural systems, and the stocks at risk such as distributions of species.  CIAS links these by collating data output from each researcher’s module into a common data format (netCDF), and allowing serial execution of the modules.  Based on open source and/or freeware tools, CIAS is the first UK project to produce such a computing framework for national and international climate policy makers. It is globally unique in allowing alternative component modules from the wide range of computer platforms and in multiple computer languages used by the various researchers to share data and be coupled together to function as unique integrated models. 

The advance that CIAS approach makes over existing integrated modeling approaches is that it is designed to test the robustness of the outputs of integrated assessment models to the use of different component modules, as well as to the values of uncertain parameters within the modules. The principal advantage of the approach is that it allows the user to compose many different individual integrated model combinations.  This is particularly important when

(i) comparing the use of individual modules of different levels of complexity and detail

(ii) comparing the use of individual modules from different institutions which have similar complexity but are based on different modeling paradigms or value judgments originating from the different institutions.

In addition, a full uncertainty analysis technique can be applied to the system holistically.  Thus for a given question one can investigate the degree to which increasing complexity of component models enhances understanding or increases/decreases uncertainty, and assess the robustness of results to paradigm shifts.  Hence the policy maker can receive a clear picture of the consistency or otherwise of integrated modeling results, the sensitivity of output to value judgments lying behind modeling paradigms, and the sensitivity of the results to uncertainties in parameter values within component modules.

CIAS is thus particularly useful to researchers who wish to understand the robustness of results, since it (i) allows alternative set of models and datasets to be linked and (ii) allows uncertainty analysis through Latin hypercube sampling across probability distributions of uncertain parameters.  

CIAS has therefore been designed according to the following principles agreed by consensus through a series of workshops with policy makers and integrated assessment scientists:

(a) It is flexible and multi-modular to allow a range of policy questions to be addressed, thus facilitating iterative interaction with stakeholders;

(b) distributed, that is deployed across a wide range of institutions in different countries, allowing a range of international expertise to be combined into single modelling framework;

(c) The system can take advantage of (but is not limited to) state of the art Grid technologies which allow models to communicate with each other remotely regardless of operating system or computer language; (d) The system is jointly owned by a community of institutions which contribute individual models or the underpinning software. The system’s name directly reflects this community approach.

CIAS currently allow various combinations of the following component modules to be connected together into alternative integrated assessment models: a probabilistic version of the global climate system module, MAGICC from the University of East Anglia; the downscaling module, ClimGen, also from the University of East Anglia; a global climate impacts module for biome shifts, which is also a component of the ICLIPS integrated assessment model from the Potsdam Institute for Climate Research in Germany; a hydrological module from the University of Reading; a biodiversity module from the University of Canterbury, a coastal model DIVA from a consortium including PIK and the University of Southampton.

There are plans to further develop the system by including further sectoral models, including climate impact response functions from the AIM integrated assessment model, and to create linkages to the GENIE climate model.One of the most exciting projects is the linkage to climate envelope modeling techniques through the Wallace initiative.

Theme Co-ordinators:Rachel Warren

Image Credit: "Squares and Rectangles" by Carlos ZGZ is licensed under CC BY 2.0