GCM simulations have/are carried out by several climate modelling groups around the world. Twenty of these groups are involved in the Coupled Model Intercomparison Project (CMIP) of the World Climate Research Programme (WCRP). They carry out coordinated GCM simulations in support of the Assessment Reports of the IPCC. CMIP4, the Coupled Model Intercomparison Project Phase 4 contributed to the 4th Assessment Report (AR4) of the IPCC (IPCC, 2007a). The GCM simulations they produced are for the greenhouse gasses increasing as observed through the 20th century (called 20C3M Figure 4.6 Simplified chart of the main processes involved in modelling hydrological impacts from climate change. Note: Dashed lines around climate-carbon cycle coupling methods indicate that not all models are coupled (after Wardet al.2011).
scenario experiment) and for the 21th century IPCC SRES scenarios. These simulations are provided by the IPCC Distribution Data Centre:
http://www.mad.zmaw.de/IPCC_DDC/html/SRES_AR4/index.html;
http://www.mad.zmaw.de/IPCC_DDC/html/ddc_gcmdata.html.
Currently (2012) the CMIP5, Coupled Model Intercomparison Project Phase 5, is ongoing. This project will deliver many more simulations for the 21st century RCP scenarios as a contribution to the 5th Assessment Report (AR5) of the IPCC. These simulations are done using GCMs which are coupled to a global ocean circulation (atmosphere-ocean general circulation models; AOGCMs).
The simulations cover given periods, both historical periods (called control simulations or baseline periods; e.g. 1961–1990) and future periods (called scenario simulations or periods; e.g. 2071–2100), or are transient for long continuous periods, for example from 1950 till 2100. The control and scenario periods are most often about 30 years or longer, because a 30-year period is likely to contain wet, dry, warm and cool periods (see Section 3.2) and thus is generally considered sufficiently long to define the climate for a given location. A 30-year “normal” period as defined by the World Meteorological Organization (WMO), for example 1961–1990, is recommended by the IPCC for use as a baseline period.
GCM modelling was once strictly limited to the specialized research groups who had access to supercomputers. However, today there are situations where the GCM simulations can be performed by enthusiasts from disciplines outside climate science. For example, EdGCM is a research-grade GCM created by Columbia University and based on the Goddard Institute for Space Studies’ General Circulation Model II, which is computationally efficient enough for use on personal computers. EdGCM has a friendly user interface, making it suitable for educational use (Chandler, 2005).
However, for the standard IPCC climate scenarios, the GCM results that are readily available from the IPCC AR4 and AR5 archives make it unnecessary to run GCM experiments for the sole purpose of obtaining the results of standard runs.
The outputs from these climate model simulations are, however, subject to significant uncertainties, due to scaling problems and to the limited knowledge of the physical processes and the obvious high uncertainties in the climate forcing scenarios. Even given the wide range of demographic, socio-economic, technological and social development scenarios considered by the IPCC, the actual future developments may differ significantly from the scenario projections. For example, the SRES scenarios that currently are most often considered do not account for the fact that populations might significantly adapt their behaviour due to climate change experiences and/or communication/sensitization. In terms of climate processes, the increase in GHGs and aerosols will lead to radiative warming, but also to cooling (the so-called global dimming effect, as discussed in Pathirana, 2008). Depending on the specific processes included in the models and their specific descriptions, the results from different GCMs differ, despite their common basis (the fundamental equations). Generally models tend to agree on the direction (i.e. sign) of the change, but differ with respect to the magnitude and/or speed of the change. This again emphasizes the importance of uncertainties in climate projections.
Another potential source of uncertainty are the initial conditions of the climate model. A GCM starts from an unknown and arbitrary initial condition in the pre-industrial era (mid-1800s). Simulations with the same type of GCM, differing only in the initial conditions, will correspond better or worse with the historical climate evolution and will give also different results for the (especially near) future. For general climate features, the uncertainty related to initial conditions of the same GCM may be as large as the uncertainty related to the type of GCM (e.g. Kjellströmet al.2010). The impact of initial conditions on precipitation extremes in the Rhine basin for different time scales down to daily was studied by Kew et al. (2011), using a set of 17 runs with the ECHAM5 GCM, forced by emission scenario A1B,
differing only in initial conditions. Future changes in daily extremes were found to differ by up to 25% for individual members. Similar conclusion was obtained by Willems and Vrac (2011), as will be shown in Chapters 7 and 8.
GCM simulations based on the RCPs were not publicly available at the time this book was prepared.
They are envisaged to be ready soon in order to enter the AR5 writing process (to be finalized in 2013/2014). As soon as sufficient climate model runs based on the RCPs become available, it is clear that additional research will be needed to study the effect of these changes in the scenarios (e.g. the effect of mitigation) on the rainfall extremes and urban drainage systems. Questions that may need to be answered are (among others): Is the range of GCM simulations, currently provided sufficiently complete, and are climate simulations available to widen the range if needed? Could impact studies based on the SRES scenarios be somehow“connected”to the new RCP process, and if not, what would be necessary to allow this? It will take time before a full evaluation becomes possible, including linking of the new scenarios selected for the AR5 to climate simulations, detailed assessment of extremes for a set of models, and a range of impact studies. The treatment of uncertainty from scenarios and models, and the possible inclusion of lower emission scenarios in future work are further discussed in the next chapter.
4.4 DISCUSSION
This chapter has discussed climate models as systems of differential equations based on the basic laws of physics, fluid motion, and chemistry. These numerical models represent and couple atmosphere, ocean, land surface and sea ice processes based on assumptions of GHG concentrations in the atmosphere, land use and other critical variables that determine the rate of physical processes in the atmosphere. Future trends in these variables are in turn estimated based on climate forcing scenarios. The changes imposed by these changes in forcing (due to changes in anthropogenic emissions) should be compared to the inherent natural variation of precipitation.
GCMs typically have poor accuracy in simulating precipitation extremes. Moreover, they produce results at spatial and temporal scales that are far too coarse for urban drainage applications. This can be done through the use of high resolution RCMs nested in GCMs. This method is called dynamical downscaling which is presented in the next chapter.
Chapter 5
Dynamical approach to downscaling of rainfall
The previous chapter explained that various types of climate models exist and are broadly classified as either GCMs or RCMs. RCMs account for the sub-GCM grid scale topographical features and land cover heterogeneities. They use initial and boundary conditions from the output of GCMs for selected time periods of the global run. This is commonly known as the nested regional climate modelling technique or dynamical downscaling. Up to now, this approach has been one-way, with no feedback mechanisms from the RCM simulation to the driving GCM. In this simulation scheme, the role of the GCM is to simulate the response of the global circulation to large scale forcing (i.e. GHG concentrations). The RCM accounts for finer scale forcing, like topographic features, in a physical manner, and enhances the simulation of the climatic variables at these finer spatial scales. Although the GCM boundaries in general have a very strong influence on the RCM results (including the effect of changes in GHG forcing on the global climate), RCMs may have a significant role during periods of convective precipitation due to the local convection effects (Rummukainen, 2010). Apart from precipitation, RCMs can provide host of other hydrometeorological variables, on the surface (e.g. solar radiation, sensible and latent heat flux) and three dimensional atmospheric space (e.g. humidity, cloud densities, temperature). These outputs are generated from the physical simulation and hence physically consistent with each other.
When using climate models in studies on climate change, sensitivity studies can give the modeller a thorough insight into the sensitivity of the model output to changes in one or more input parameters or their drivers. Such sensitivity analysis is often undertaken through controlled numerical experiments.
Some examples are impact of sea-surface temperature on storm generation over coastal areas, impact of urban growth (as a driver of land use change) on changes of urban microclimates leading to altered rainfall patterns, impact of atmospheric aerosols on rainfall processes and impact of mountains on extreme rainfall.
In this chapter, a brief overview is given of the dynamic downscaling approach (Section 5.1), followed by a discussion of the main tools used in that type of dynamic downscaling by means of RCMs (Section 5.2).
Specific attention is given to the method of calculating precipitation (extremes) in these models. Also the concepts of nesting, how local data can be used to improve dynamic downscaling with RCMs, and sensitivity studies, are discussed. Several case studies on fine-scale rainfall simulation from recent literature are presented (Section 5.5) that may help the reader to better understand the concepts discussed in this chapter.