GCAM v5.3 Documentation: GCAM Fusion users guide

Documentation for GCAM
The Global Change Analysis Model

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GCAM Fusion users guide

Here we discuss the GCAM Fusion API modelers tools to perform two way coupling with a running GCAM simulation. We start with an introduction then move on to some more in-depth documentation some of which may only be relevant for someone who is interested in modifying or adding new components to GCAM itself.

Note: the discussion that follows is aimed at an audience proficient with C++. GCAM developers looking to understand how to make their GCAM module complaint with GCAM Fusion should check the Developer Guide.


GCAM is a object oriented model using a hierarchical structure to represent the various sectors and activities that it models. This is convenient for setting up the abstractions and relationships with in the model; however it does not make it easy or convenient to get data in and out of the model. We generally set inputs data once at the start of the model by parsing the XML input files.  We retrieve data mostly through the use of custom visitors at the end of a scenario, or after the run has completed via an XML database.

These limitations have been a hinderance for modelers who would like to implement one-way or two-way coupling bwteen their own model and GCAM, in which data from the coupled models is pushed into GCAM from a high level so as to dynamically incorporate feedbacks simulation moves forward through time.

The goal for GCAM Fusion is to be able to control the internal parameters of GCAM from a high level. However, most users will not be familar with GCAM object and member variable names. They are usually familiar with the XML tag names used in the input/output which typically map directly on to those internal variables. In addition many of our users have grown accustomed to searching the XML via simple XPath queries that look like: /scenario[@name='Reference']//sector[@name='electricity]//share-weight[@year <= 2050]. Thus GCAM Fusion is a query engine for GCAM with a query syntax somewhat like XPath using the same data names as the XML input tags. Users can then use the search results as they like including changing the value of the results.

Providing a hook to calling feedbacks

Currently we are providing the following hooks for users to call their feedbacks:

Limiting access to the API in this way is a key part of our strategy for keeping the model structure manageable. Arbitrary updates to model internals can happen only at designated times. This prevents every model object from becoming, in effect, a global variable.

You create a feedback calculation by creating a “feedback object”, which is an instance of a class that implements IModelFeedbackCalc interface. The key functions in the interface are

The full definition of IModelFeedbackCalc is:

 * \ingroup Objects
 * \brief This provides the interface for classes which will provide feedback to
 *        model parameters as calculated based upon model results as the simulation
 *        moves forward through the model years.
 * \details This interface will provide two hooks to notify when to calculate feedbacks:
 *          Before a new model period is about to start and after a model period has
 *          has finished solving and climate model results for the period are available.
 *          A references to the Scenario and IClimateModel will be provided however
 *          it is implied that the subclasses of this interface will utilize the 
 *          GCAM Fusion capabilities to gain access to the internal model state
 *          necessary to compute and/or push feedbacks into GCAM.
 * \warning MAGICC does not currently run between periods so these feedbacks may
 *          not work correctly if climate results are needed and MAGICC is configured
 *          as the climate model.
 * \author Pralit Patel
class IModelFeedbackCalc : public INamed,
                           public IParsable,
                           public IRoundTrippable,
                           private boost::noncopyable
    virtual ~IModelFeedbackCalc() { }
     * \brief A call back to indicate that a new simulation period is about to begin.
     * \details Note that climate model results will not yet be available for the current.
     *          period however results up to where the year where the model left off
     *          from the prior model year should still be available.
     * \param aScenario The Scenario object that contains the full state of the currently
     *                  running model.
     * \param IClimateModel The climate model instance which can be queried for climate
     *                      results.
     * \param aPeriod The model period that is about to begin calculation.
    virtual void calcFeedbacksBeforePeriod( Scenario* aScenario, const IClimateModel* aClimateModel, const int aPeriod ) = 0;
     * \brief A call back to indicate that a simulation period has ended and the climate
     *        model has been run updated through this period.
     * \param aScenario The Scenario object that contains the full state of the currently
     *                  running model.
     * \param IClimateModel The climate model instance which can be queried for climate
     *                      results.
     * \param aPeriod The model period that just finished it's calculation.
    virtual void calcFeedbacksAfterPeriod( Scenario* aScenario, const IClimateModel* aClimateModel, const int aPeriod ) = 0;

An Introduction Using an Illustrative Example

In order to explain how to use the GCAM Fusion capabilities let’s jump right into it with an illustrative feedback example. In this example we will:

Such a usage pattern will likely be common. More specifically, in this example we will query for global CO2 emissions, calculate feedbacks to heating and cooling degree days using a simplistic linear relationsip, and finally change the heating and cooling degree days with in GCAM for the next simulation period.

To start we will create a new class which implements the feedback interface mentioned above.

#include "containers/include/imodel_feedback_calc.h"

 * \ingroup Objects
 * \brief Calc feed back to heating and cooling degree days.
 * \details Test implementation.
 * \author Pralit Patel
class DegreeDaysFeedback : public IModelFeedbackCalc
    virtual ~DegreeDaysFeedback();
    static const std::string& getXMLNameStatic();
    // INamed methods
    virtual const std::string& getName() const;
    // IParsable methods
    virtual bool XMLParse( const xercesc::DOMNode* aNode );
    // IRoundTrippable methods
    virtual void toInputXML( std::ostream& aOut, Tabs* aTabs ) const;
    // IModelFeedbackCalc methods
    virtual void calcFeedbacksBeforePeriod( Scenario* aScenario, const
                                            IClimateModel* aClimateModel, const int aPeriod );
    virtual void calcFeedbacksAfterPeriod( Scenario* aScenario, const
                                           IClimateModel* aClimateModel, const int aPeriod ); 
    //! The name of this feedback
    std::string mName;
    //! A HDD feedback coefficient of sorts
    double mHDDCoef;
    //! A CDD feedback coefficient of sorts
    double mCDDCoef;
    //! The base year emissions value to calculate feedback from
    double mBaseYearValue;

You will notice that we have also included the XML parsing hooks that GCAM uses to initialize its components such as XMLParse and toInputXML. These functions allow us to activate our feedback by including them in an XML add-on file

The source code that goes with this declaration will then look like the following skeleton:

#include "util/base/include/definitions.h"
#include <cassert>
#include <vector>

#include "containers/include/degree_days_feedback.h"
#include "util/base/include/xml_helper.h"
#include "containers/include/scenario.h"
#include "util/base/include/model_time.h"

using namespace std;
using namespace xercesc;

:mHDDCoef( 0 ),
mCDDCoef( 0 ),
mBaseYearValue( 0 )

DegreeDaysFeedback::~DegreeDaysFeedback() {

const string& DegreeDaysFeedback::getXMLNameStatic() {
    // This is the string you will use to refer to this object
	// in input files.
    const static string XML_NAME = "degree-day-feedback";
    return XML_NAME;

const string& DegreeDaysFeedback::getName() const {
    return mName;

bool DegreeDaysFeedback::XMLParse( const DOMNode* aNode ) {
	// Code to read the feedback object from XML inputs

void DegreeDaysFeedback::toInputXML( ostream& aOut, Tabs* aTabs ) const {
	// Code to write the object's configuration as XML 
	// (This is used when saving a configuration to be reread later)

void DegreeDaysFeedback::calcFeedbacksBeforePeriod( Scenario* aSceanrio, 
                                                    const IClimateModel* aClimateModel, 
													const int aPeriod ) 
    // code that gets called just before a period will begin to solve

void DegreeDaysFeedback::calcFeedbacksAfterPeriod( Scenario* aScenario, 
                                                   const IClimateModel* aClimateModel,
                                                   const int aPeriod )
    // code that gets called after a period is done solving, 

The two XML functions allow us to set up our feedback object from GCAM XML input files. Here is how they are defined:

bool DegreeDaysFeedback::XMLParse( const DOMNode* aNode ) {
    /*! \pre Make sure we were passed a valid node. */
    assert( aNode );
    // get the name attribute.
    mName = XMLHelper<string>::getAttr( aNode, XMLHelper<void>::name() );

    // get all child nodes.
    DOMNodeList* nodeList = aNode->getChildNodes();
    // loop through the child nodes.
    for( unsigned int i = 0; i < nodeList->getLength(); i++ ){
        DOMNode* curr = nodeList->item( i );
        string nodeName = XMLHelper<string>::safeTranscode( curr->getNodeName() );
        if( nodeName == XMLHelper<void>::text() ) {
        else if( nodeName == "hdd-coef" ) {
            mHDDCoef = XMLHelper<double>::getValue( curr );
        else if( nodeName == "cdd-coef" ) {
            mCDDCoef = XMLHelper<double>::getValue( curr );
        else {
            ILogger& mainLog = ILogger::getLogger( "main_log" );
            mainLog.setLevel( ILogger::ERROR );
            mainLog << "Unknown element " << nodeName << " encountered while parsing " << getXMLNameStatic() << endl;
    return true;

void DegreeDaysFeedback::toInputXML( ostream& aOut, Tabs* aTabs ) const {
    XMLWriteOpeningTag( getXMLNameStatic(), aOut, aTabs );

    XMLWriteElement( mHDDCoef, "hdd-coef", aOut, aTabs );
    XMLWriteElement( mCDDCoef, "cdd-coef", aOut, aTabs );
    XMLWriteClosingTag( getXMLNameStatic(), aOut, aTabs );

With these functions in place, you will be able to activate the feedbacks by including an XML add-on file in your GCAM configuration. Including the add-on file will cause the feedback object to be created and added to the scenario’s list of feedbacks. The calcFeedbacksBeforePeriod and calcFeedbacksAfterPeriod methods will then be run automatically at the beginning and end of each GCAM time step. The add-on file would contain the following XML:

<?xml version="1.0" encoding="UTF-8"?>
	<!-- The numerical values for the coefficients here are just examples -->

Next we will add in some calls to GCAM Fusion to query for the global CO2 emissions from the model. You will need to include the following header files into your .cpp file:

#include "util/base/include/gcam_fusion.hpp"
#include "util/base/include/gcam_data_containers.h"

Be aware that including GCAMFusion essentially includes the entire model.  This is because it needs to be able to search and traverse potentially any object in the model.  This will lead to a long compile time for any source file that includes it. Therefore, you should try to isolate code that uses these capabilities in a small number of fusion-aware translation units.

Next, you will need an object that will handle the results of the search. This object can be of any type, since the GCAMFusion class is templated. The only requirement is that the object must provide the templated functions that will be called on data found by the search. The functions required will depend on what combination of the three event types supported by GCAM Fusion you are using. The event types eare explained below. If you aren’t using an event type, you can omit its processing function.

struct GatherEmiss {
    // a variable to keep the sum
    double mEmiss = 0;

    // call back methods for GCAMFusion
    // called if the fourth template argument to GCAMFusion is true
    template<typename T>
    void processData( T& aData );

    // call back methods for GCAMFusion
    // called if the second templated argument to GCAMFusion is true
    // we won't be using it in this example.
    //template<typename T>
    //void pushFilterStep( const DataType& aData );

    // call back methods for GCAMFusion
    // called if the third templated argument to GCAMFusion is true
    // we won't be using it in this example.
    //template<typename T>
    //void popFilterStep( const DataType& aData );

We now have everything we need to use the GCAM Fusion interface. The GCAMFusion object takes four template parameters:

The last flag (the one that defaults to true) is the most common use case, and it’s the only one we will use in this example.

The GCAMFusion object constructor takes two arguments:

Then we can call GCAM Fusion with a search string and have it use the above struct to process the results:

void DegreeDaysFeedback::calcFeedbacksAfterPeriod( Scenario* aScenario, const IClimateModel* aClimateModel,
                                                   const int aPeriod )
    vector<FilterStep*> emissFilterSteps = parseFilterString( "world/region/sector/subsector/technology/period[YearFilter,IntLessThanEq,"+
        modeltime->getper_to_yr( aPeriod )+"]/ghg[NamedFilter,StringEquals,CO2]" );
    // notice we can search by a year by using the YearFilter or by a GCAM model period by just using
    // an IndexFilter
    emissFilterSteps.push_back( new FilterStep( "emissions", new IndexFilter( new IntEquals( aPeriod ) ) ) );
    GatherEmiss gatherEmissProc;
    // note we are just using the default template flags here: just process data, not the steps
    GCAMFusion<GatherEmiss> gatherEmiss( gatherEmissProc, emissFilterSteps );
    // We must provide an object as the context to start the search, in this case we
    // start at the top with the Scenario object.
    gatherEmiss.startFilter( aScenario );
    // Results are not returned and instead the processData callback function of the
    // GatherEmiss class is called when a matching emissions value is found.

    // We can then retrieve the result to use it in our impact calculations 
    double currGlobalEmiss = gatherEmissProc.mEmiss;
    cout << "Curr global emissions are " << currGlobalEmiss << " in period " << aPeriod << endl;

As you can see above the FilterSteps can be created manually or by using the utility parseFilterString to conveniently generate it for you by using a syntax similar (but not precisely identical) to XPath. Each filter step may contain a data name and a filter . Each filter contains a predicate and the predicate value.

As mentioned above, when GCAMFusion finds a result that matches the search it will call processData and the user can get or set the value as appropriate for their needs:

template<typename T>
void GatherEmiss::processData( T& aData ) {
    assert( false );
void GatherEmiss::processData<Value>( Value& aData ) {
    mEmiss += aData;

GCAMFusion cannot know what the type of the result of the search is going to be ahead of time.  Searches are made at runtime while the code to handle the results are generated at compile time.  This is the reason the processData method must be templated.  Therefore, we create a template specialization for the type we are expecting to be returned from our search (i.e., based on our prior knowledge of the model structure). In this case we expect the appropriate type to be a Value class, so we provide a specialization for that type. If everything is working correctly, we shouldn’t get any other type. If we do, then we’ve made a mistake in setting up the system. Therefore, in the generic template, which will be instantiated for any other types that might be returned by the search, we assert that the code should never get there during runtime. If for some reason it does, then the run will abort with an error.

Next we do something with our results. To keep things simple for illustrative purposes, we’ll adjust degree days by a scale factor, but you could in principle do anything here, including running another model and passing it the data you just received.

    if( aPeriod == modeltime->getFinalCalibrationPeriod() ) {
        // just store the base year value
        mBaseYearValue = currGlobalEmiss;
    // scale heating and cooling degree days for the next period
    mCurrDDScaler = 1.0 / ( currGlobalEmiss / mBaseYearValue ) * mHDDCoef;

Finally we can query for the appropriate GCAM paramaters again but this time changing the value. You will notice that really everything works the same as when we were collecting the CO2 emissions. The data passed to processData is passed by reference to the actual parameter that lives in the GCAM objects and is not const so we are free to change it as we please. These are the queries for building heating and cooling services:

    // Note the actual services are "resid heating" or "comm cooling", etc so we
    // use regular expression partial matching so we do not have to spell it out.
    vector<FilterStep*> ddFilterSteps = parseFilterString( "world/region/consumer/nodeInput/nodeInput/nodeInput[NamedFilter,StringRegexMatches,heating]" );
    ddFilterSteps.push_back( new FilterStep( "degree-days", new IndexFilter( new IntEquals( aPeriod + 1 ) ) ) );
    GCAMFusion<DegreeDaysFeedback> scaleHDD( *this, ddFilterSteps );
    scaleHDD.startFilter( aScenario );
    mCurrDDScaler = ( currGlobalEmiss / mBaseYearValue ) * mCDDCoef;
    // only updating the service name filter of our query, we can keep the rest of it the same
    delete ddFilterSteps[ ddFilterSteps.size() - 2 ];
    ddFilterSteps[ ddFilterSteps.size() - 2 ] = new FilterStep( "nodeInput", new NamedFilter( new StringRegexMatches( "cooling" ) ) );
    GCAMFusion<DegreeDaysFeedback, true> scaleCDD( *this, ddFilterSteps );
    scaleCDD.startFilter( aScenario );

    // note we are still responsible for the memory we allocate even if it was done in the parseFilterString utility
    for( auto filterStep : ddFilterSteps ) {
        delete filterStep;

And here are the call backs that set the scaled degree days in those sectors:

template<typename T>
void DegreeDaysFeedback::processData( T& aData ) {
    assert( false );
void DegreeDaysFeedback::processData<Value>( Value& aData ) {
    // We are manipulating aData which is referenced back to the actual GCAM objects
    aData *= mCurrDDScaler;

void DegreeDaysFeedback::pushFilterStep( INamed* const& aContainer ) {
    std::cout << "Saw step " << aContainer->getName() << std::endl;

template<typename T>
typename boost::disable_if<
    boost::is_base_of<INamed, typename boost::remove_pointer<T>::type>,
void>::type DegreeDaysFeedback::pushFilterStep( const T& aContainer ) {
    std::cout << "Saw unknown " << typeid( T ).name() << std::endl;

A couple things to note, sometimes it is easier to just use your feedback object to process callbacks from GCAM Fusion. It isn’t required to use a helper struct. Your class/struct does not need to implement any interface or the like, just provide the processData, etc callback methods.

I have also included and configured the call back for pushFilterStep just for example. In addition you will notice the use of disable_if and some uses of boost’s (using the std library should work just fine too) type traits such as is_base_of. The purpose is to demonstrate how to control which objects we are intereasted in our pushFilterStep call back. In this example we have the compiler generate one version for any object for which we can call ->getName() on (implements the INamed interface) and another for types which do not.

Linking to Other Models

Often you want to use the data you retrieve using the GCAM Fusion interface as input to another model. Likewise, you may want to return the results of running that model to GCAM as feedbacks. To do these things you will have to link your model to GCAM. There are three options for doing this.

Run your model as a stand-alone program and communicate with GCAM via IPC

This method is a little clunky, but it is probably the easiest to get up and running in most cases. You will run your model as a stand-alone process, and you will communicate with GCAM via interprocess communication (IPC).

The easiest form of IPC to get up and running is the named pipe. (The linked article is for Linux, but named pipes work the same on Mac OS X as they do on Linux.) The concept here is that you will have set up a named pipe for each communication stream between GCAM and your model. These communication streams are unidirectional, so for two way communication you will always need at least two. Once the pipes are set up you can read and write to them as if they were files. Your model should expect to receive data from GCAM after each GCAM time step on the input pipe, and it should write its data on the output pipe. You will also need to establish conventions for signaling in-band when the data for a single time step is finished, and when a model is exiting.

On the GCAM side, you will put all of the code to communicate with your model in one of the calcFeedbacksBeforePeriod or calcFeedbacksAfterPeriod methods associated with a feedback object, as described in the previous section. The code in the method should collect the data you want from GCAM objects, format it as required by your model, and write it to the named pipe your code is using for input. Then you will want to read your model’s response on the pipe your code is using for output. This operation will block until your model provides its response.

Next, you must recompile GCAM including your new feedback object and add its XML add-on file to your GCAM configuration. Then, to perform a run, execute both models (e.g., from two terminal windows). The models should communicate with one another whenever they have data to send, and block whenever they are waiting for the other model to provide data they are expecting.

The advantage of this method is that it can be set up with relatively little modification to either model’s software. The disadvantage is that one must manage the communication between the models carefully to avoid deadlock, a situation in which both processes are simultaneously stopped waiting for input from the other process.

Build your model as a library and have GCAM call it

To use this option you will take the object files produced by compiling your model, and collect them into a library. Building libraries works a differently depending on what operating system you are using. On unix-like operating systems, including OS X, you can create them using the ar command. When you build this library, make sure you leave out the main() function from your program. You will be using GCAM’s main(), and trying to build a program with two mains will cause an error.

Once you have your library you will add that to the list of libraries that GCAM links to. Once you have done this, any functions in your model will be callable from GCAM. You will probably have two types of functions you will want to call: functions to initialize your model and functions to run your model components. Initialization functions can be called from GCAM’s main. Functions to run your model’s components should be called from the methods of GCAM feedback objects, either calcFeedbacksBeforePeriod or calcFeedbacksAfterPeriod, as appropriate. When these callbacks run they will have access to the data returned by filters and can provide them to your model. Similarly, data returned by your model can be set into objects returned by the filter step. Once you have written the necessary feedback objects you can recompile GCAM, and the linked models should be ready to run.

The advantage of this method is that if your model already supports accepting data from other models, you will be able to use it with little or no modification. Your model will not need any particular knowledge of GCAM’s internal structure and might even be indifferent to whether it is getting data from GCAM or from some other source. The disadvantage of this method is that it requires you to make some changes to GCAM, particularly where initializing your model is concerend.

This is the method used to implement the one-way coupling between GCAM and Hector, so looking at that model may provide some guidance on how to proceed. However, one difference between Hector and other models is that Hector predates the GCAM Fusion interface. Therefore, instead of being called from a filter object, Hector is called from a hook built into GCAM specifically for that purpose. Future model coupling will take place through GCAM Fusion, eliminating the need for custom hooks.

Have your model call GCAM functions provided in libgcam.

This option is the inverse of the previous one. Instead of building your model as a library, you link your model to the libgcam.a library created when GCAM is built. All of GCAM’s functions will be available to be called from your model. You will need to call GCAM initialization functions to set up the model structure. After that, the Scenario::run() method will allow you to run individual time periods in the scenario. You can call Scenario::run() at any point in your model where it makes sense to do so; you can even run a period multiple times with different feedbacks from your model in each evaluation, if doing so is useful in the problem you are trying to solve.

Using this method, you will not need to create a feedback object. Instead, you can create a handler object and use it to call the GCAM Fusion interface directly from anywhere in your code. You can create multiple handlers and multiple filters and use them as required to get or set data from GCAM.

This method gives you a lot more control over how and when GCAM runs than other methods. It is also the best method for coupling to models that have very complex setup procedures, since it avoids having to replicate the setup within GCAM. The main disadvantage to this method is that it requires a lot of GCAM-specific modifications to your model. These modifications will have to be disabled to use your model in stand-alone mode or to couple to another model.

Intended Use of GCAM Fusion

Note that GCAM Fusion gives the users full access to all the internal parameters of GCAM for better or for worse. Just because you are able to change these values doesn’t mean GCAM will be able to operate normally when doing so. Therefore we only reccommend using GCAM Fusion inside of the IModelFeedback methods. Making feedbacks during the solution of a model period would require additional dependencies and linkages to ensure proper solution and GCAM Fusion would entirely circumvent those procedures. As a rule of thumb adjusting the same model perameters which are parsed in GCAM XML input files should be fine to modify. It should not be used to curcimvent normal object orientened principals or designs. Object encapsulation allows us to ensure some level of consistency.

To be clear there are no sofware limitation imposed on the use of GCAM Fusion however code proposed for inclusion into the Core GCAM model may be rejected due to improper / abuse of the capabilities as it will hinder the long term maintainability of the model.