This is part of the labels / documentation for <a href='http://jcm.chooseclimate.org'>Java Climate Model</a><hr/>

#mitigpanel		¨oldlink		§Old JCM Link to @stabilisation, @aboutsres, @othgasemit, @distribution

#emitmenu		¨oldJCM4		§Choose a future mitigation policy option. See also @stabilisation

#mitigation		¨oldJCM4		§This Module contains the calculations described by<li>@stabconcmethod,<li>@stabitmethod,<li>@stabemitmethod,   <li>The general principles are described in topics linked from @stabilisation

Mitigation module applies a policy feedback from the chosen target indicator (e.g. Concentration or Temperature) back to the Emissions. Thus it is at the heart of JCM's interacting modules , as shown by the @flowchart.

#nopolicy		¨oldJCM4		§See @sresmenu, @aboutsres,

#constant		¨oldJCM4		§This option may be useful to simplify investigation of different @distribution options, or to illustrate the @inertia in the climate system. ;;

Note that this only affects total CO2 emissions (fossil + landuse together). To ensure that emissions of other gases are also constant, choose £`2000fix from the @othgasemitoptions

As the emissions are not affected by anything else, this option may also be a useful basis for scripting custom scenarios. See (old!) @emitdeclinescript for an example.

#stabemit		¨oldJCM4		§Set a target CO2 emissions stabilisation curve, defined by a stabilisation year and level (and optionally, integral). ;;
  For more explanation see: {{ *:@stabemitdoc
*:@stabemitmethod
*:@stabilisation }}

#integral		¨oldJCM4 fix		§Note: this label has multiple use, need new name for the stab-emit param

#initgrow		¨oldJCM4		§see @stabemitmethod

#integralopt		¨oldJCM4		§see @stabemitmethod

#stabconc		¨oldJCM4		§Set a target curve for @atco2conc, defined by stabilisation year and level 
(also, optionally, initial growth and decline after peaking at that level)
The formula for the curve is the same as that used for fixed IPCC scenarios (see @stabconcmenu). ;;
For more explanation see: {{<li>@stabconcdoc<li>@stabconcmethod<li>@atco2conc,<li>@stabilisation
}}

#stabconcmenu		¨oldJCM4		§This menu of CO2  predefined stabilisation scenarios adjusts the parameter of @stabconc  to generate the same target concentration curves as in  IPCC S (="WG1") or WRE scenarios (see @wreopt). ;;

For more explanation see<li>@stabconcdoc<li>@stabconcmethod<li>@stabilisation
  €€cogs %%(Note: 400ppm and 500ppm were not in IPCC, but are calculated by the same method as the others)%%

#wreopt		¨oldJCM4		§This option, which derives from the proposal of Wigley Richels & Edmonds (Nature, 1995), delays the start of the @stabconc curve,  setting the initial emissions to follow a business as usual scenario (IS92A). Combining this option with scenarios from the @stabconcmenu will generate WRE stabilisation curves similar to those used in several IPCC reports.
  For further explanation see @wredelaystart,  @stabconcmethod

#stabconcstartyear		¨oldJCM4		§scriptable parameter (no graphical control, but adjusted by @wreopt)

#stabrf		¨oldJCM4		§For more explanation see:{{
*:@stabrfdoc, 
*:@stabitmethod
*:@radfor
*:@stabilisation }}

#co2eq		¨oldJCM4 addJCM5		§CO2 Equivalent Concentrations are calculated from the current radiative forcing by inverting the relationship between forcing and CO2 concentration - for more explanation see   @radfor
  %%€€adju The CO2 Equivalent curves appear on @atco2plot when £`expert is chosen from the @complexitymenu  %%
  Note that the contribution of the other gases depends strongly on the option chosen from @othgasemit, and the scenario chosen from @sresmenu
  You can choose to stabilise £`co2eq using  £`stabrf -see @stabrfdoc

Although @art2 requires stabilisation of concentrations of greenhouse gases (note: plural),  there is not yet any consensus on the definition of CO2 equivalents for use in stabilisation scenarios. Which gases should be included (three alternatives are shown on @atco2plot)? Should we consider just the current forcing, or the integral of future impacts?

One problem is that the gases have very different  lifetimes  in the atmosphere -from a few weeks for tropospheric ozone and about ten years for CH4 (see @othgasplot), to about 100 years for CO2, and even to several  thousand years for SF6 (see @fgasplot). Moreover these lifetimes are changing due to various feedback processes. For example, the lifetime of CH4 is affected by OH (see @oghga), and the lifetime of CO2 depends on the response of ocean and biosphere sinks (see @carbonstoreplot).

Fixed Global Warming Potentials are not appropriate in the  context  of long-term stabilisation because: 
{{ <li> GWPs are defined in terms of emissions, not concentrations  <li> GWPs  only consider the integral of radiative forcing over a fixed time horizon (e.g. 100 years), which is an arbitrary policy decision.   <li> GWPs  should change over time, even disregarding evolution of the science, because of feedback effects on the lifetimes of gases (see above), which will vary between scenarios.  <li> GWPs  do not adequately represent the effects of short-lived unevenly distributed forcings from aerosols and ozone.
}}

More-inclusive, longer-term replacements for the GWP concept are being investigated. The problem is similar to that of @attribution inspired by the Brazilian proposal, which also compares the climate impact from different sources of emissions (see also @attributeplot, @responsibility).
Note also the @radiative_forcing_index used for aviation emissions.

#6gas		¨oldJCM4		§The 'six' gases controlled by the Kyoto protocol are CO2, N2O, CH4, SF6, PFCs, HFCs  <li>See also @co2eq

#allghg		¨oldJCM4		§This adds Tropospheric and Stratospheric Ozone, and CFCs to  @6gas.
 Beware that the distribution of ozone forcing is different to that of well-mixed greenhouse gases

#allghgaero		¨oldJCM4		§This includes all forcing from anthropogenic emissions, adding sulphate and carbon aerosols to @allghg. 
Beware that the distribution of aerosol forcings is very different to that of greenhouse gases, so one cannot say that positive forcing (from GHGs) in one region is cancelled by negative forcing (from aerosols) in another: the result is still climate change!

#stabtemp		¨oldJCM4		§Set a target year and level to  stabilise @glotempcurves 
€€cogs This works iteratively by adjusting a CO2 concentration curve ;;
€€cogs  Note that the temperature is relative to the @baseyear ;;
  For more explanation see:
{{<li>@stabtempdoc<li>@stabitmethod<li>@stabtemp2c<li>@glotempcurves<li>@stabilisation}}

#stfuzzyopt		¨oldJCM4		§This option enables @stabtempfuzzy, as an experimental alternative to the default @stabitmethod

#dampopt		¨oldJCM4		§see @stabtempfuzzy

#stabsea		¨oldJCM4		§Set a target  year and level to stabilise @sealevelrise 
€€cogs This works iteratively by adjusting a CO2 concentration curve
€€cogs It is difficult to stabilise sea-level due to the inertia in the climate system. The model may not find a good solution. ;;
  For more explanation see:{{<li>@stabseadoc, @stabitmethod<li>@sealevelplot<li>@stabilisation}}

#reduceintensity		¨oldJCM4		§This illustrates the long-term implications of a recent US proposal.
  It shows what happens, if the <b>ratio CO2 emissions per dollar GDP</b> (known as <i>emissions intensity</i>) is reduced by a prescribed percentage each year. The default rate is -2% per year (as US proposal). You can adjust this with the control (enabled from @emitmenu) which appears on a @distribplot of CO2 emissions. The same <i>intensity</i> reduction applies to all regions and all future years, but the regional GDP data changes according to the SRES scenario (see @sresmenu, @regiondatasource).

#stabconnect		¨oldJCM4		§The original IPCC "S" (or WG1) and "WRE" scenarios both started from 1990, whereas in this model the future starts at 2000. The total fossil CO2 emissions at 2000 were about 6.7Gt/yr, slightly higher than the original "S" scenarios at 2000, and slightly lower than projected under the IS92A scenario (7.1Gt/yr) which is used for the initial phase of WRE. Thus  It is sometimes stated that we must use the WRE scenarios because  we have lost too much time and 'S' are already history ? however the figures show that our current development path actually lies between the original WRE and 'S' pathways, so both sets need updating, but neither is more valid.

For both sets of scenarios, JCM starts from the present emissions (also scaling IS92A down by 6.7/7.1) in order to avoid a discontinuity which looks odd, and would have strange effects on the model. This correction may also have a small impact on the emissions peak, compared to that calculated with the original profiles (this problem was not apparent in IPCC, because they don't show you history and future on the same graphic!).

  %%Note: To get back to the original WG1 pathway from the present, would require rather abrupt reductions initially (this may partially explain why the economic mitigation costs shown in IPCC-TAR-SYR-Q7  seem rather high for the WG1 pathway. The choice of "discount rate" would also strongly influence any comparison of pathways, since discounting reduces later compared to earlier costs.)%%

  The SRES scenarios (see @aboutsres) are not scaled, although they are also slightly higher than current emissions.

#stabtemp2c		¨oldJCM4		§In 1996 the European Council of Ministers proposed a long-term climate policy target, that the global average temperature rise should not exceed a limit of  2C above the preindustrial level, and <i>therefore</i>, that the CO2 concentration should be stabilised at less than 550ppm. This policy has not been superceeded, however the climate science has evolved since 1996,  so further investigation of the implications may be valuable.

€€adju You can explore this in JCM using the @stabtemp on @glotempplot (enable from @emitmenu). The default stabilisation level is already set to 2C, however the default @baseyear is 1990 (for consistency with IPCC) so you must first adjust this to preindustrial. %%Note that the preindustrial level is not precisely defined as there is some natural climate variability, try to find a mid-range level between 1750 and 1900.%%

 The resulting emissions and concentration pathways are rather sensitive to uncertainties in the climate model (@heatflux), and to assumptions about other gas emissions (@othgasemit).
  €€adju Try experimenting with the @climodmenu and @othgasemit (combined with @sresmenu). Since the final temperature should remain constant, so the emissions and concentrations must change accordingly.

The @stabtemp2cscript does this for you automatically.
  You can see that there is a very wide range of uncertainty, regarding the implications for short term emissions reductions. This poses a challenge for policymakers, what do you think is a 'safe' pathway? On the other hand, choosing a temperature rather than a concentration target helps to reduce the uncertainty in the consequent climate change impacts. See also @uncertburden

  <hr>There is also a wide range of CO2 concentrations (see @atco2plot),  but the middle of this range lies between 450ppm and 500ppm. So it would seem that the EU figure of 550ppm was rather "optimistic". However we should recall that this policy was made based on science from IPCCSAR (Second Assessment Report), whereas JCM is based on the more recent IPCCTAR. One of the key differences is that the SAR used the old scenario IS92A, which projected much higher emissions of sulphate aerosols, whose large cooling effect offset some of the global warming due to greenhouse gases. You can see this, by picking IS92A from @sresmenu. The TAR also includes some additional forcings, which were not in the SAR. The climate model in the TAR was tuned to a range of 7 GCMs, however the average of these is not much different to the single model used in the SAR (which is also included in the @climodmenu).

Nevertheless, these scientific changes do not explain all of the discrepancy. We should also recall, that early research and policy on anthopogenic climate change focussed mainly on CO2, whilst the effect of other gases was considered later. Consequently, many policymakers think of other gases in terms of CO2 equivalents (although this is not well defined in the stabilisation context -see @co2eq). You can see curves for @co2eq on the @atco2plot (only at @expert complexity level) which are much closer to the EU's figure of 550ppm than those for CO2 alone. So it might seem reasonable to interpret this figure as referring to CO2 equivalent concentration.

Considering also convergence between regions (see @distribution), this would imply reducing EU emissions by about 75% by 2050.

#stabtemp2cscript		¨dem		§This demonstration script shows how JCM  may be used to explore different pathways to keep the temperature rise below 2C (compared to preindustrial), which is the EU's long-term policy target. It illustrates the problem of  uncertainty in inverse stabilisation calculations.  <li>See @stabtemp2c for an introduction to this topic.  <li>These results were presented at the European Geophysical Society conference in Nice, April 2003. The inspiration was in response to a meeting of the "European Group on Further Action".
  £!stabtemp2cscript
  €€cogs The script may take about five minutes to run on a typical PC. The model  should not be distrurbed during this process. Note that for every curve, the model must iterate several times (see @stabitmethod).
  <hr>

For all the 98 curves, the @stabtemp is set to stabilise at 2C by 2150, and the @baseyear is set to 1850.

The 98 variants come from the combination of the 7 GCMs (parameterised as in the TAR -see @gcmfit) with 14 options for the emissions of non-CO2 gases.
  The greener curves correspond to cooler GCMs. The baseline is preindustrial, hence there is already divergence at 2000. 
  €€cogs %%Note: uncertainty in the carbon cycle / biogeochemical feedbacks is not considered%%

The 14 other gas options are the combination of the options in @othgasemit and @sresmenu. The redder set of 49 curves have no mitigation of non-CO2 gases, whilst the other 49 assume that emissions of each gas (including aerosol and ozone precursors) are reduced by an equal proportion, compared to the SRES baseline in each year (see @sresscale).  

For a few variants (hottest GCMS, largest other  gas emissions) the iteration failed to find a pathway below 2C, these are shown in grey.

For comparison, you might like to check out @stabconc500script
  <hr>
  It is acknowledged, that presenting such a wide range of pathways would not be particularly helpful for policymakers. However, we have to start by understanding why scientists may give apparently contradictory interpretations of a simple policy target, as a first step towards reducing the uncertainty range.
  Another JCM-script is under development, to see how this wide uncertainty range may be constrained by a probabilistic approach based on the fit to historical data. (see @probabilistic) 

  <hr>See @scripting for an explanation of how to adapt the code.

#stabconc500script		¨dem		§This script runs the same set of 98 scenarios as described in @stabtemp2cscript. See also @stabconcdoc, @scripting
  £!stabconc500script

#stabilisation		¨oldJCM4 addJCM5		§This module calculates top-down, inverse scenarios created to stabilise a specific climatic indicator at a target level.

JCM can produce many types of flexible multi-gas stabilisation scenarios, based on target indicators at different stages of the cause-effect chain.

The fundamental principle derives from @art2 
£§art2short 

For more introduction to the concept, see: @stabdoc

#emitcc		¨oldJCM4 addJCM5		§Note this is old documentation from JCM4, to be updated
<ul>
<li>@stabilisation
%%("Where do we need to go?")%%
  The aim of the UN Climate Convention is to avoid dangerous climate change, by stabilising concentrations of greenhouse gases. You can investigate a range of levels and pathways to stabilise CO2 concentration, forcing, or temperature directly.   <li>@aboutsres
%%("Where are we going?") %%
  There are many possible pathways of future development, and hence greenhouse gas emissions, even assuming no specific policy to avoid climate change. Explore the causes and consequences of the range of IPCC-SRES "baseline" scenarios.
</ul>
  ---- ===Related topics === 
<ul> <li>@philosophy (comparing above approaches)  <li>@distribution    <li>@othgasemit   <li>@effectofkyoto  <li>@equity  <li>@optimisation
</ul>

#art2short		¨oldJCM4		§Article 2 of the UN Climate Convention (UNFCCC ) states that its ultimate aim is:
  <i>"...to stabilise concentrations of greenhouse gases in the atmosphere at a level which will prevent dangerous anthropogenic interference with the climate system,"</i>

#art2		¨oldJCM4		§££art2short

It continues: <i>"Such a level shall be achieved within a time-frame sufficient to allow  ecosystems to adapt naturally to climate change, to ensure that food production is not threatened, and to enable economic development to proceed in a sustainable manner"</i>
  <hr>This is a laudable aim, however its interpretation raises many questions,  some of which may be explored with JCM
(Old JCM4 doc, to be updated...)
<ul>  <li>What is "dangerous" climate change? Impacts are very unevenly distributed, so  dangerous for whom, where and when? See @impacts, @inertia, @equity  <li>Considering uncertainty in climate science, at what probability might dangerous  impacts be acceptable? See @uncertainty, @uncertburden  <li>"concentrations of greenhouse gases"  is plural  -how do we combine their effects? See @othgasemit, @co2eq, @stabrfdoc  (all gases)  <li>What is the easiest emissions pathway to reach this level - how fast should stabilisation occur? See @stabpathways, @wredelaystart  <li>Is concentration the best indicator for a stabilisation target?  See  @stabrfdoc, @stabtempdoc for alternatives
</ul>
The global debate to balance these factors requires insight from complex models, however policy targets are likely to focus on a few simple indicators - for example see @stabtemp2c.

@stabitmethod describes the iteration mthod for many JCM  scenarios. This  illustates a more general point-  when designing an efficient iteration algorithm for inverse calculations, the correction-feedback process is more important than the initial guess. This also applies to the global iteration between scientists, policymakers and citizens, essential for interpreting Article 2. So we should not fear making bold guesses, but need to design better feedback in the global dialogue (see @dialogue, @concept)

#inverseintro		¨oldJCM4		§The emissions are adjusted to reach the target level. So the model is working backwards, from the effect, to the cause (see @inverse), as well as forwards to the climate impacts (see @stabimpact).

#inverse		¨oldJCM4		§Stabilising concentration, Radiative Forcing, Temperature, or Sealevel  are all examples of <i>inverse</i> calculations, starting from a specified destination, and calculating backwards (from desired effect to required cause) to find a pathway to reach it.
  %%See<li>@stabconcdoc, @stabrfdoc, @stabtempdoc, @stabseadoc%%

It should be emphasised that stabilisation scenarios are not <i>predictions</i>. They are useful for exploring <i>mitigation</i> policies, whereas the SRES scenarios may be more appropriate when exploring <i>adapation</i> policies.

Inverse calculations may also seem confusing, when considering the effect of scientific uncertainties. For example, when the target CO2 concentration is fixed, and  you adjust scientific uncertainty parameters affecting the ocean or biosphere carbon sinks,  the emissions change to keep the CO2 concentration on target. As there is also a biogeochemical feedback between the temperature and the carbon sinks, adjusting climate model parameters can also change the emissions.

So if you want to explore cause-effect relationships within the natural carbon-climate system, it is recommended to use either @stabemit (for low emissions) or @nopolicy (for high emissions).  <li>See also:  @philosophy, @flowchart

#stabimpact		¨oldJCM4		§The @glotempplot and @sealevelplot illustrate the climate impact of different stabilisation levels. Beware that regional, seasonal climate changes can be much greater than global average figures, as illustrated by the @regclimap.

Although there are many uncertainties in the carbon and climate models, lowering the stabilisation level always reduces the impact (see @uncertainty).

The temperature rise slows quite soon after CO2 stabilisation, but continues to increase slowly due to the gradual transfer of heat to the deep ocean (@oceantempplot, @rftemp).  The inertia is much more apparent in the sea-level, which continues to rise for centuries after CO2 stabilisation (see @inertia) .
  ££stabimpactdemo  <li>See also @impacts
  <hr>
  You may also wish to consider the socioeconomic impact of emissions reductions
  (see @abate, @distribution, @regshares, @costs)

#stabimpactdemo		¨oldJCM4		§<i>JCM Demonstrations are  currently being rewritten </i>

#stabipccsyrq6		¨oldJCM4		§IPCC-TAR Synthesis report Question 6 contained a well-known graphic of CO2 stabilisation profiles, based on the WRE scenarios   <li>See <a href="../doc/pic/syrspm6.jpg" target="pic"> IPCC Synthesis Report, figure SPM6</a>, also @ipcclinks.

You can recreate these scenarios in JCM by combining the @stabconcmenu and the @wreopt.

However, in order to calculate the impact on temperature and sea-level, an assumption must also be made about the contribution of the other greenhouse gases to radiative forcing.
  For this purpose IPCC-TAR SYR Q6 assumed that emissions of other gases are fixed according to SRES A1B scenario.
  To reproduce this, you should choose £`sresfix from the @othgasemit menu, and "A1B" from the @sresmenu.   <li>The @othgasemit offers some alternative assumptions, including mitigation of all greenhouse gases.  <li>Note also  @ipccothgas

#stabpathways		¨oldJCM4		§There are many possible pathways towards any given stabilisation level.

To satisfy the conditions of the second part of @art2, we have to find a pathway that avoids abrupt changes, balancing climatic and economic considerations. If we reduce emissions more earlier, we don't have to reduce so dramatically later. On the other hand, later reductions may be eased by new technologies, if we make an effort to develop these now. This is a question of <i>"intergenerational equity"</i> -what kind of legacy should we leave to future generations (see also @equity).  Moreover we mus consider the inertia in the socioeconomic system - it takes a long time to change some key factors which influence emissions ? such as planning of cities, and transport and energy infrastructure.

You can explore some variants by dragging the endpoint of the target stabilisation curve horizontally (move the 4-pointed  £`control) , thus changing the timing of stabilisation without changing the final level. This applies to all stabilisation controls in JCM. For @stabconc, you can also adjust  the start of the stabilisation curve  using the @wreopt.

The choice of pathway makes little difference to the long-term equilibrium climate impacts, but it does influence the rate of temperature rise, which affects the ability of ecosystems and society to adapt. It may also strongly affects the economic costs of mitigation   <li>See also @wredelaystart, @impacts, @ costsplot

#stabrelated		¨oldJCM4		§Stabilisation scenarios may be combined with other emissions options (menus in the @mitigpanel):  <li>@distribution : Any reduction from the no-climate-policy baseline to approach a stabilisation target requires a global agreement to share the limited budget between countries. Investigate this controversial but critical question. Any mitigation scenario may also be combined with @kyotoopt, which applies the Kyoto Protocol targets for AnnexB countries up to 2013.  <li>@othgasemit : JCM has several options to link the emissions of other gases with those of CO2, noting that @art2 tells us to "stabilise concentrations of <i>gases</i>", not only CO2. Investigate how much difference this makes -noting that these options are also dependant on the @sresmenu.
  Note also @scalelanduse
  <hr>

#ipccothgas		¨oldJCM4		§Methane (CH4), Nitrous Oxide (N2O), Tropospheric Ozone (O3), HFCs and CFCs are important greenhouse gases.
  So the £~sres  specify emissions of CH4, N2O, HFCs, also of NOx, VOC, and CO which lead to the production of ozone, and of sulphate and carbon aerosols (watch @othgasplot, while adjusting @sresmenu).

 In IPCC-TAR Synthesis report Q6 (see @ipccsyrq6) it was assumed that emissions of other gases are fixed according to SRES A1B no-policy scenario, for all the CO2 stabilisation levels  (see @stabilisation).

€€adju You can reproduce  this in JCM by choosing £`sresfix from the @othgasemit menu.

In this case, the other gases contribute as much to the radiative forcing as 28% extra CO2 (since the CO2 radiative forcing is a logarithmic function of concentration, this applies to any level).

However this is not the default option in JCM, because it seems rather unrealistic, that we would make a big effort to stabilise CO2 concentrations, without also mitigating emissions of other gases.

%%Note that with the IPCC assumption (SRES A1B fixed) 450ppm CO2 corresponds roughly to 550ppm CO2-equivalent (all gases together -see @co2eq), leading to a temperature rise of about 2oC (an upper limit proposed by the European Union). Whereas if you assume option (b) Equal % of SRES A1B as CO2, you can reach this temperature target stabilising CO2 at about 500ppm. See also @stabtemp2c %%

#stabemitdoc		¨oldJCM4		§Many people talk about reducing CO2 emissions to a "sustainable level".
  You can explore this concept, by choosing £`stabemit from the @emitmenu in the @mitigpanel. Then on a @distribplot you can see that total CO2 emissions follow a simple mathematical curve, starting  from the present level and trend, and eventually stabilising at a time and level determined by the 4-pointed yellow arrow control.
  %%€€adju (@stabemit  works the same way as @stabconc on @atco2plot)%%

For example it is often quoted, that we need to reduce global emissions by about 60% to stabilise the concentration of CO2 at current levels. This figure derived from the first IPCC report (1992) can be explained by considering that 3/5 of the fossil CO2 emissions stayed in the atmosphere whilst 2/5 was taken up by the ocean sink, while land-use sources and sinks rouhgly cancelled (these ratios are still approximately valid today -see @carbonstoreplot).

You can test whether this works, by moving the moving the yellow arrow to stabilise CO2 emissions at 2.4 GtC/yr in 2010. This makes the atmospheric CO2 concentration (black curve on carbon cycle plot) level off at about 375ppm, but only <i>instantaneously</i>. If you look further into the future, the concentration  starts to rise again, since the sinks reduce as the rate of atmospheric CO2 increase falls, the biosphere sink saturates, and the seawater becomes more acidic. If we really want to stabilise the atmospheric CO2 concentration, we have to keep reducing emissions for centuries, which is apparent when you choose instead the @stabconc option, and place the black arrow on @atco2plot at 375ppm in 2010.

%%€€adju Note, if you want to stabilise emissions at current levels, simply select £`constant from the @emitmenu%%

At the "expert" @complexitymenu, you can also "fine-tune" the emissions stabilisation curve, by adjusting the @initgrow and @integral.

The calculations are made in @mitigation, for more explanation see @stabemitmethod.
  ££stabrelated

#stabemitmethod		¨oldJCM4		§This formula simply fixes the global CO2 emissions according to a mathematical curve which is defined by:   <li>emissions in the start year (2000, or 2013 after @kyoto)   <li>@initgrow (adjustable parameter at @expert complexity level)   <li>target level in the stabilisation year (set by @stabemit)   <li>gradient in the stabilisation year (zero)
  These constraints are sufficient to define a unique cubic curve.
  If the @integralopt is selected (expert level), you can also adjust the @integral, to define a quartic curve.
  <hr>
  The calculations are made in the @mitigation module.

#stabconcdoc		¨oldJCM4		§The concept of stabilising CO2 concentration at a "safe" level is enshrined in Article 2 of climate convention (see @art2).
  £`stabconc is also the default option in JCM's @emitmenu (in @mitigpanel).
  £`stabconc sets a target CO2 concentration curve according to a simple mathematical formula
  %%€€adju (Note: atmospheric CO2 concentration is the black curve shown on the @atco2plot, measured in ppm on the right hand scale).%%
  You can adjust the stabilisation level and year, either by dragging the  black 4-pointed arrow (@stabconc) which appears on the @atco2plot.
  Alternatively you can use the @stabconcmenu which reproduces fixed IPCC scenarios (in which the stabilisation year is 2100 + (stabilisation level - 450) / 2
  Both methods may also be combined with the @wreopt.
  ££inverseintro

The mathematical formula,  based on that used for the original IPCC stabilisation scenarios, is the same for all these profiles, but they have different start and end points. For further explanation see @stabconcmethod.
  The calculation is made in @mitigation module.
  ££stabrelated  <li>See also @stabipccsyrq6
  ££stabconcdemo

#stabconcdemo		¨dem oldJCM4		§<i>JCM Demonstrations are being rewritten</i>

#stabconcmethod		¨oldJCM4		§The calculations are made in the @mitigation module.

First, a target concentration curve is set, using the Pade formula defined by the IPCC Technical paper of Enting et al 1994 (see @sciref).
  This is a ratio of two quadratic polynomials, whose constants are defined by:  <li>initial concentration (c<sub>0</sub> at t<sub>0</sub>),   <li>initial concentration gradient (dc<sub>0</sub>/dt<sub>0</sub>)    <li>initial d2c<sub>0</sub>/dt<sup>2</sup><sub>0</sub> This constraint avoids a kink in the emissions curve. It has the same effect as the arbitrary parameter described in the Enting paper, but can be more easily generalised.   <li>final concentration (c<sub>s</sub> at t<sub>s</sub>) The stabilisation level and year are set by @stabconc or @stabconcmenu   <li>final concentration gradient  (dc<sub>s</sub>/dt<sub>s</sub>)  The final gradient is zero for CO2 stabilisation (although not for stabilisation of other indicators -see @stabitmethod)  <li>%%€€cogs Note: in the current JCM implementation an additional constraint has been added, that the final d2c<sub>s</sub>/dt<sup>2</sup><sub>s</sub> is also zero, thus defining a quintic curve. This helps with stabilisation of other indicators, it makes little difference for CO2%%

The initial year, level and gradient  may be affected by the @wreopt -see @wredelaystart, and also by the @kyotoopt -see @kyoto. Note also @stabconnect
  <hr>
  Having set the target curve, the emissions are then calculated in each timestep (year), as the change in concentration, plus the ocean and biosphere sinks. It is assumed that the sinks will change by the same amount as in the previous timestep (i.e. their second derivative is constant, during this part of the calculation). The model is then run to calculate the actual sinks and concentration as normal, including feedbacks (see @carboncycle). Any deviation from the target is corrected in the next step. This sink assumption works well in a "smoothly" changing model (if the target curve is plotted, you can barely see the difference from the actual curve).
  However it might not work in a GCM which includes rapdily changing natural climate variability, due to the large impact of climate-carbon feedbacks.

Total emissions are then shared between fossil fuels and land use change (see @scalelanduse)

#scalelanduse		¨oldJCM4		§For all the stabilisation options, the calculated total CO2 emissions are distributed between fossil fuel and land-use change, using the same constant fractions as in the starting year. This is preferable to fixing land-use CO2 emissions according to SRES, which causes a rather jagged curve for the remaining fossil CO2 emissions. Alternative options may be added later.

#wredelaystart		¨oldJCM4		§IPCC considered two alternative sets of CO2 stabilisation pathways:
  the original formula from the IPCC 1994 technical paper, known as "S" or "WG1" scenarios, and a variant on this developed later by Wigley, Richels and Edmonds (1995), known as "WRE" scenarios, which can be enabled using the @wreopt.
  %%(note: this option cannot be combined with @kyotoopt)%%

The WRE scenarios follow the IS92A "business as usual" pathway for an initial period of 10-30 years, before curving away to reach the stabilisation target. The delay is longer for higher levels, according to the formula:
  start year = 2002 + (stabilisation level - 350) / 23.0
  The starting level is scaled to be consistent with current emissions: see @stabconnect

As you can see, the WRE option allows higher emissions initially, but later they must drop more steeply.
  WRE suggested that it might be economically more efficient to delay initial emissions reduction, although they did not apply any economic optimisation model in developing these scenarios. On the other hand, they also stressed that although emissions reductions might be delayed, the effort to develop new technology and infrastructure, anticipating reductions later, should begin immediately.

%% Note that, as WRE pointed out, the rate question is complicated by the short-term cooling effect of sulphate aerosols which are a by-product of burning coal. Considering this, the WRE pathway can actually lead to slightly cooler <i>global average</i> temperatures for the first few years! €€adju (note you will only see this subtle effect if applying @othgasemit %%
  <hr><li>See also paper by Wigley Richels & Edmonds, Nature 1995, and @stabpathways

#stabrfdoc		¨oldJCM4		§When £`stabrf is chosen from the @emitmenu (in @mitigpanel), a four pointed arrow control appears on @radforplot which controls the  stabilisation level and year of a target radiative forcing curve, which includes the forcing from either @allghg or @allghgaero. Note that these curves are only shown at expert @complexitymenu.
  %%€€adju (@stabrf works the same way as @stabconc on @atco2plot)%%
  ££inverseintro

The calculations are made in the @mitigation as descibed in @stabitmethod.

Why might we want a target to stabilise radiative forcing? @art2 tells us to stabilise concentrations (plural), but not how to combine the effects of different gases. Stabilising radiative forcing is effectively the same as stabilising @co2eq, in a way that is simply defined (although the concept is not easily explained -see @radforintro).
  We could also include all gases by stabilising the Temperature, which is a more tangible indicator closer to climate impacts, however that would shift the large uncertainty regarding the @climsens onto the resulting  emissions pathway (see @stabtempdoc, @uncertburden). So stabilising Radiative Forcing may be a compromise, using an indicator in the middle of the cause-effect chain (see @flowchart).

%% €€adju  Making the radiative forcing constant also helps to illustrate how the surface temperature lags behind the forcing, due to the slow penetration of heat into the deep ocean. (see @rftemp, @glotempplot, @oceantempplot).%%
  ££stabrelated

#stabitmethod		¨oldJCM4		§Stabilisation of Radiative Forcing, Temperature or Sea-Level are all achieved using the same iterative method to find an emissions pathway which reaches the desired target. (%%see @stabrfdoc, @stabtempdoc, @stabseadoc%%)
  An iterative method is necessary because many factors combine in the radiative forcing,  some of them interacting by feedback processes, so it would be difficult to find a direct analytical solution to the inverse calculation.

Each time through the iteration loop, the CO2 concentration is set by two mathematical curves, before and after the stabilisation year (as defined below). The CO2 emissions are calculated from this, using the same assumptions about the carbon sinks as described in @stabconcmethod. The emissions of other gases and aerosols may then be scaled to CO2 emissions, depending on the option selected from @othgasemit. Now it has all the emissions, the model calculates everything forwards as usual, as far as the indicator that should be stabilised (forcing, temperature, sea-level). Three correction factors are then calculated to adjust the CO2 concentration curve, which are applied in the next iteration step. The iteration continues until the correction factors are smaller than a threshold (1% for the stabilisation level), or the maximum number of iterations has passed (currently 12).

The iteration must start with an intelligent guess of the CO2 concentration curve. For example, the radiative forcing is calculated from the required surface temperature increase using the climate sensitivity (assuming equilibrium and  ignoring heat exchange with the ocean), and 85% of this forcing is attributed to CO2. This is scaled by a the correction factor remembered from the last such calculation. The initial guess curves are displayed briefly on the model while it is iterating.  If you make a a large change to the parameters (such as changing the model from @climodmenu),  you will see that the initial guess may be far from the target, and the iteration takes longer time to find a better curve. On the other hand if you make a small change by dragging an arrow control, the initial guess is quite close due to the remembered correction factor, so the performance is good.

Up to the stabilisation year, the concentration curve is defined by a quintic curve: 

<nobr>y = ( a + b.x + c.x<sup>2</sup> + d.x<sup>3</sup> ) / ( 1 + e.x + f.x<sup>2</sup> ) </nobr>
  The six parameters a-f are fixed by solving the following constraints:  <li>initial concentration (c<sub>0</sub> at t<sub>0</sub>),   <li>initial gradient (dc<sub>0</sub>/dt<sub>0</sub>)    <li>initial d2c<sub>0</sub>/dt<sup>2</sup><sub>0</sub>   <li>final concentration (c<sub>s</sub> at t<sub>s</sub>)   <li>final gradient  (dc<sub>s</sub>/dt<sub>s</sub>)    <li>final d2c<sub>s</sub>/dt<sup>2</sup><sub>s</sub> (= zero)
  This is a variant of the Padé formula as used in @stabconcmethod, and the initial values are fixed in the same way. The difference here is that the CO2 concentration gradient is not flat in the stabilisation year.

Between the stabilisation year and 2300 (the final year in JCM), the concentration is defined by a simpler quadratic curve fixed by three constraints: the initial level and gradient (same as final level/gradient above), and the final level.

The concentration and gradient in the stabilisation year, and the concentration in 2300, are the three factors which are corrected by the iteration loop (described above).

More complex formulae were tested, but did not produce better results. Providing extra degrees of freedom may fulfill the criteria better at the specified points, but cause strangely shaped curves between them.
  <hr>  <li>The calculations are part of the @mitigation, the curve algebra is in @mathcurve  <li>Note also the @stabtempfuzzy method

#stabtempdoc		¨oldJCM4		§If you choose £`stabtemp" from @emitmenu (in @mitigpanel), a four-pointed arrow appears on the @glotempplot, which  fixes the stabilisation year and level for a target global average temperature curve.  The temperature level  is relative to the baseline defined by the @baseyear.
  %%€€adju (@stabtemp works the same way as @stabconc on @atco2plot)%%
  ££inverseintro

Although @art2 refers to stabilising concentrations, it could be argued that a target for stabilising temperature would be closer to the real climate impacts which we are trying to avoid, and so reduces the uncertainty for the receivers of these impacts. On the other hand, this transfers the effect of large uncertainties in the @climsens and related factors onto the range of possible emissions pathways.    <li>@stabtemp2cscript illustrates the wide range of pathways  <li>@stabtemp2c discusses this issue further.   <li>See also @uncertburden  <li>Even disregarding uncertainties, there could be many possible ways to reach  a particular stabilisation level - see @stabpathways

The calculations are made  in the @mitigation as descibed by @stabitmethod.
  %% €€cogs An alternative experimental method works by "fuzzy-control" correcting the emissions in each timestep according to the deviation from the target curve (€€adju choose @stabfuzzyopt at experimental @complexitymenu).  This method  tends to produce oscillations.-see @stabtempfuzzy.  %%

  ££stabrelated
  Beware also, that regional temperature changes could be much higher than the global average-see @regclimap

#stabtempfuzzy		¨oldJCM4 addJCM5		§An @experimental module (experts only!), exploring what may happen if we try to stabilise the temperature by applying a feedback control from temperature to emissions (without foresight) 
This module is only activated when @stfuzzy is selected from @indicator in @stabilisation

see old label: @fuzzycontrol
below is old documentation to be updated
----

%%Note: this method is only available at @experimental complexity level as it can produce some strange results! Use the @stfuzzyopt to enable it. The default method in JCM as described in @stabitmethod is much more reliable, although slower. %%
  This method first sets a target temperature curve, according to a formula similar to that used for @stabconcmethod (€€adju this target curve is shown on the temperature plot). The emissions in each year are then adjusted slightly according to the deviation from the target temperature.
  If the other gas emissions are also mitigated proportionally to CO2 (see @othgasemit), oscillations arise due to the "destabilising" effect of sulphate aerosols (on a short timescale, the sulphate cooling effect is greater than the CO2 warming effect), whereas if these gases are fixed by SRES scenarios, the kinks in the scenarios cause corrective kinks in the CO2. However this formula works well, if the other gas emissions are constant at 2000 levels.

#fuzzycontrol		¨oldJCM4		§The greek word cybernaut means 'helmsman', and the problem of steering ships is a useful analogy for climate policy. A big ship has much momentum so we cannot change course instantly, but we still need to find a strategy for responding to buffeting by wind and waves, changing weather and contradictory instructions, to steer a comfortable passage towards the destination.

Deliberate dynamic climate => emissions feedbacks may be used as a basis for a long-term policy formula which adjusts in response to changing climate science or observations. This aims to reduce the effect of uncertainties. If the climate warms more than expected, the global emissions budget should decrease, and vice-versa. Such an approach might also satisfy sceptics, who would not expect much warming.

In any year, the rate of change of emissions may be adjusted as a function of the recent rate of change of temperature or atmospheric CO2 (or other criteria). However, the slow response from emissions to impacts can lead to oscillations: this approach a bit like "steering a supertanker by eye down a narrow channel in the fog"!  A better formula would combine observation and prediction (a navigator should also use charts and instruments), calculating the rate emissions change based on the deviation from a scientifically modelled target temperature curve, and filtering misleading short-term effects.   <li>see also @stabtempfuzzy, @philosophy
  <hr>%% (note some overlaps with philosophy page: needs reorganising!) %%

#stabseadoc		¨oldJCM4		§When £`stabsea is chosen from the @emitmenu (in @mitigpanel), a four pointed arrow control appears on @sealevelplot which fixes the  stabilisation level and year of a target sea level curve.
  %%€€adju (@stabsea works the same way as @stabconc on @atco2plot)%%
  ££inverseintro

The calculations are made using the @stabitmethod, in the @mitigation

You will soon discover that this control does not succeed well, so it is provided for educational purposes, rather than as a serious policy option. It is almost impossible to stabilise the sealevel because it responds so slowly to surface temperature rise, due to the slow penetration of heat into the deep ocean and into the polar ice-caps (see also @inertia, @oceantempplot, @sealevel).

On the other hand, some low-lying countries have proposed that eventually we should think beyond stabilising temperature rise (see @stabtempdoc),  and try to reduce the long-term impacts of sea-level rise by continuing to reduce the temperature after it peaks.
  ££stabrelated

#uncertburden		¨scc		§For any specified emissions pathway, there is a large range of possible climate change impacts, due to the combination of many uncertain factors in both the biogochemical cycles, and the climate system response and feedbacks. Thus, even if we had challenging long-term emissions reductions targets, those who have to adapt to climate change would have to cope with this uncertainty (some change is inevitable due to the large inertia in the system -see @inertia).

On the other hand, if we had a long-term target defined in terms of acceptable climate change impacts, and a commitment to keep on track towards this by adjusting the emissions as the science evolves, the uncertainty for those adapting to climate change would be reduced, although the uncertainty for those planning emissions reductions would be increased.

In a high-level policy debate it is likely that an indicator such as global average temperature would be used as a proxy for real impacts, as in the EU's long-term policy target (see @stabtemp2c). The resulting uncertainty in emissions pathways is illustrated graphically by the @stabtemp2cscript.

So, shifting the target to an indicator later in the cause effect chain (set at an equivalent level in the best-guess case) has the effect of shifting the burden of managing uncertainty away from the receivers of climate impacts, towards the controllers of emissions.  Since those most vulnerable to climate impacts tend to be in poorer countries, and those who produce most of the emissions are mainly in the rich countries, this has some equity implications (see @equity).

Economists have long acknowledged that uncertainty is expensive, because we have to choose  investments now, guessing which may produce the optimal return in an uncertain future. For this reason some have argued in favour of policy instruments  closer to the action of emissions reduction such as "emissions intensity" targets (see @reduceintensity) or carbon taxes.  On the other hand, uncertainty regarding adaptation will also be expensive, particularly if it involves large shifts in population, agriculture or infrastructure.  So an emissions intensity approach might seem efficient for the biggest polluters, but very inefficient from the point of view of those who suffer the consequences of climate change. The inverse applies to a climate-impacts target approach.   <li>See also @stabilisation, @uncertainty @moscow @wccc2003 @probwccc

#stabconcimpactdemo		¨dem		§<i>JCM Demonstrations  are being rewritten</i>

#integralgt		§The integral of CO2 emissions from the future start year (currently 2013) until the model end year (typically 2300)
€€cogs Note: if you change the model end year, you may have to adjust the integral to get a reasonable curve.