by Judith Curry
“Representative concentration pathways” is the new phrase for what the IPCC used to refer to as “emissions scenarios.” Lets take a look at the new RCP’s being used for the AR5.
From the CMIP5 web page
Founded in 1972, the International Institute for Applied Systems Analysis (IIASA) is an international research organization that conducts policy-oriented research into problems that are too large or too complex to be solved by a single country or academic discipline:
- problems like climate change that have a global reach and can be resolved only by international cooperative action, or
- problems of common concern to many countries that need to be addressed at the national level, such as energy security, population aging, and sustainable development.
Version 2.0 of the database includes harmonized and consolidated data for three of the four RCPs. This comprises emissions pathways starting from identical base year (2000) for BC, OC, CH4, Sulfur, NOx, VOC, CO and NH3. In addition, harmonized well-mixed GHG emissions of the RCPs have been added for the period 2005 to 2100. Radiative forcing and concentrations of GHGs are given for the RCPs up to the year 2100, and are extended for climate modeling experiments to 2300 (ECPs). Wherever available, historical information is provided back to the year 1850.
Characteristics and guidance
The RCPs are not new, fully integrated scenarios (i.e., they are not a complete package of socioeconomic, emissions, and climate projections). They are consistent sets of projections of only the components of radiative forcing that are meant to serve as input for climate modeling, pattern scaling, and atmospheric chemistry modeling. As such, they jump-start the scenario development across research communities from which uncertainties about socioeconomic, climate, and impact futures can be explored. They thus constitute just the beginning of the parallel process of developing new scenarios for the IPCC’s fifth Assessment Report. By doing so, the RCPs aim at providing a consistent analytical thread across communities.
The RCPs are named according to their 2100 radiative forcing level as reported by the individual modeling teams. The radiative forcing estimates are based on the forcing of greenhouse gases and other forcing agents – but does not include direct impacts of land use (albedo) or the forcing of mineral dust.
The RCPs are not forecasts or boundaries for potential emissions, land-use, or climate change. They are also not policy prescriptive in that they were chosen for scientific purposes to represent the span of the radiative forcing literature at the time of their selection and thus facilitate the mapping of a broad climate space. They therefore do not represent specific futures with respect to climate policy action (or no action) or technological, economic, or political viability of specific future pathways or climates.
The RCPs are four independent pathways developed by four individual modeling groups. The socioeconomics underlying each RCP are not unique; and, the RCPs are not a set or representative of the range of potential assumptions. For instance, the RCPs with lower radiative forcing (RCP 6.0, RCP 4.5 and RCP 3-PD) are not derived from those with higher radiative forcing (RCP 8.5, or even RCP 6.0). The differences between the RCPs can therefore not directly be interpreted as a result of climate policy or particular socioeconomic developments. Any differences can be attributed in part to differences between models and scenario assumptions (scientific, economic, and technological). This is in particular relevant for scenario elements that are only indirectly coupled to the radiative forcing targets such as land use/land cover and air pollutant emissions.
The extension of the scenarios beyond 2100 will be done using simple algorithms intended for use as pathways to drive long-term earth-system simulation experiments and is not the result of integrated assessment analysis or modeling.
Overview in Climatic Change
An overview paper on RCP was just published in Climatic Change:
Detlef P. van Vuuren, Jae Edmonds, Mikiko Kainuma, Keywan Riahi, Allison Thomson, Kathy Hibbard, George C. Hurtt, Tom Kram, Volker Krey, Jean-Francois Lamarque, Toshihiko Masui, Malte Meinshausen, Nebojsa Nakicenovic, Steven J. Smith and Steven K. Rose
Abstract. This paper summarizes the development process and main characteristics of the Representative Concentration Pathways (RCPs), a set of four new pathways developed for the climate modeling community as a basis for long-term and near-term modeling experiments. The four RCPs together span the range of year 2100 radiative forcing values found in the open literature, i.e. from 2.6 to 8.5 W/m2. The RCPs are the product of an innovative collaboration between integrated assessment modelers, climate modelers, terrestrial ecosystem modelers and emission inventory experts. The resulting product forms a comprehensive data set with high spatial and sectoral resolutions for the period extending to 2100. Land use and emissions of air pollutants and greenhouse gases are reported mostly at a 0.5 × 0.5 degree spatial resolution, with air pollutants also provided per sector (for well-mixed gases, a coarser resolution is used). The underlying integrated assessment model outputs for land use, atmospheric emissions and concentration data were harmonized across models and scenarios to ensure consistency with historical observations while preserving individual scenario trends. For most variables, the RCPs cover a wide range of the existing literature. The RCPs are supplemented with extensions (Extended Concentration Pathways, ECPs), which allow climate modeling experiments through the year 2300. The RCPs are an important development in climate research and provide a potential foundation for further research and assessment, including emissions mitigation and impact analysis.
The complete paper is available online [here].
Tim Worstall @ Forbes
Tim Worstall has a post at Forbes entitled “Solving Climate Change.” Worstall argues ironically that the solution to the climate change problem lies within the assumptions made in developing the RCP scenarios.
My general points can be made quite simply with the aid of two of their charts.
We know very well that there’s a connection between economic growth and population size. Richer countries on average have lower fertility rates so as the world becomes richer fewer children are born. So more economic growth leading to peaking and declining population really isn’t a surprise at all.
However, look at that light green line. The RCP 2.6 one, the “whew, we dodged it” one. The highest economic growth model leads to the lowest level of emissions considered. Less economic growth leads to higher emissions.
Note again that these are not my assumptions. They are those of the IPCC process. Which is something of a body blow to those telling us that we must cease economic growth if calamity is to be averted: the very assumptions built into the whole proof that climate change is something we should worry about say exactly the opposite. Economic growth is the way out, not the problem.
The second chart:
This is how much energy we’re going to use and where we’re going to get it from. We need to be more parsimonious in our use of energy, yes. We need to use less of it per unit of GDP (which is known as “energy intensity” and their desired decrease in that isn’t far off what the advanced economies already manage) but we don’t actually need to use less of it overall. Less oil, yes, but we can near double our energy consumption and still hit that “we missed the problem” sweet spot. It’s also amusing to note what a small role for solar and wind power is necessary to hit that target.
Again, I want to point out that these aren’t my assumptions, they’re not made up out of whole cloth by some denialist, these are the assumptions which the very scientists who tell us about climate change themselves think are the driving forces and likely outcomes.
Which leads to a very interesting conclusion indeed. We don’t have to stop economic growth at all, we can quite happily have around the same amount of it that we had in the 20 th century. So that’s a large number of the Green Miserablists shown to be wrong. We don’t have to reduce or even severely limit our energy consumption: we just have to get the growth in our consumption from other than the usual sources. A large number of the Energy Miserablists shown to be wrong there too.
Or, to boil it right down, the IPCC is telling us that the solution to climate change is economic growth and low-carbon energy generation.
JC’s 5 cent solution
I don’t have a solution to the climate change problem, but I do have a proposal for developing emissions scenarios. This goes back to my previous post on emissions scenarios (the discussion on that post got hijacked by my reference to the precautionary principle.):
So how might the IPCC proceed in this regard? First, the complicated models that develop emissions scenarios don’t seem to be necessary for forcing the climate models; simply specifying a value of CO2 concentration (with the other greenhouse gases and anthropogenic aerosol) at 2100 along with a simple time trajectory is sufficient to force the climate model. The value of the emission models would be in establishing the “barely feasible” worst case scenario and the conditions under which this scenario might be created, and in rejecting more extreme scenarios.
The individual scenarios in the IPCC scenario suites (both SRES and AR5) are implicitly regarded as equally plausible. Armed with Kaya’s identity (or the more sophisticated emission models), modal falsification, and the possibility distribution, it seems that there is a feasible and credible method for establishing the relative likelihood of the different radiative forcing scenarios. Inverse modeling using Kaya’s identity could identify the number of different pathways among the various combinations of possible input variables that could result in a specific radiative forcing scenario (say +/- 10%) . The number of different combinations of variables that would produce a particular forcing scenario would provide some sense of the likelihood of that scenario (with the barely feasible scenario having only one combination of variables, and so being the least likely). A further embellishment could be provided by ranking the input values for at least some of the input variables in terms of their likelihood (from necessary to barely feasible). This would provide a rationale for the size of the bar (on the possibility to necessity scale) related to that particular scenario.
This would be much simpler and cover a broader range of possibilities for understanding the model sensitivity to different magnitudes and rates of atmospheric composition change. The overwhelming uncertainties associated with inputs to the models of representative concentration pathways don’t seem to me to justify much else.