The third Climate Dialogue is about the value of models on the regional scale. Do model simulations at this level have skill? Can regional models add value to the global models?
We have three excellent participants joining this discussion: Bart van den Hurk of KNMI in The Netherlands who is actively involved in the KNMI scenario’s, Jason Evans from the University of Newcastle, Australia, who is coordinator of Coordinated Regional Climate Downscaling Experiment (CORDEX) and Roger Pielke Sr. who through his research articles and his weblog Climate Science is well known for his outspoken views on climate modelling.
Are climate models ready to make regional projections?
Climate models are vital tools for helping us understand long-term changes in the global climate system. These models allow us to make physically plausible projections of how the climate might evolve in the future under given greenhouse gas emission scenarios.
Global climate projections for 2050 and 2100 have, amongst other purposes, been used to inform potential mitigation policies, i.e. to get a sense of the challenge we are facing in terms of CO2 emission reductions. The next logical step is to use models for adaptation as well. Stakeholders have an almost insatiable demand for future regional climate projections. These demands are driven by practical considerations related to freshwater resources, especially ecosystems and water related infrastructure, which are vulnerable to climate change.
Global climate models (GCMs) though have grid scales that are quite coarse (>100 km). This hampers the reconstruction of climate change at smaller scales (regional to local). Regions (the size of e.g. the Netherlands) are usually covered by only a few grid points. A crucial question therefore is whether information from global climate models at this spatial scale is realistic and meaningful, in hind cast and/or for the future.
Hundreds of studies have been published in the literature  presenting regional projections of climate change for 2050 and 2100. The output of such model simulations is then used by the climate impacts community to investigate what potential future benefits or threats could be expected. However several recent studies cast doubt whether global model output is realistic on a regional scale, even in hind cast. [2-5]
So a legitimate question is whether global and/or regional climate models are ready to be used for regional projections? Is the information reliable enough to use for all kinds of medium to long term adaptation planning? Or should we adopt a different approach?
To improve the resolution of the models other techniques, such as regional climate models (RCMs), or downscaling methods, have been developed. Nesting a regional climate model (with higher spatial resolution) into an existing GCM is one way to downscale data. This is called dynamical downscaling. A second way of downscaling climate model data is through the use of statistical regression. Statistical downscaling is based on relationships linking large-scale atmospheric variables from either GCMs or RCMs (predictors)and local/regional climate variables (predictands) using observations. 
Both methods are widely used inside the regional modelling community. The higher spatial resolution allows a more detailed representation of relevant processes, which will hopefully, but not necessarily, result in a “better” prediction. However RCMs operate under a set of boundary conditions that are dependent on the parent GCM. Hence, if the GCM does not do an adequate job of reproducing the climate signal of a particular region, the RCM will simply mimic those inaccuracies and biases. A valid question therefore is if and how the coupling of a RCM to a GCM can provide more refined insights. [7,8]
Recently Kerr  caused quite a stir in the regional modelling community by raising doubts about the reliability of regional model output. A debate about the reliability of model simulations is quickly seen as one between proponents and sceptics of anthropogenic global warming. However as Kundzewicz  points out “these are pragmatic concerns, raised by hydrologists and water management practitioners, about how useful the GCMs are for the much more detailed level of analysis (and predictability) required for site-specific water management decisions (infrastructure planning, design and operations).”
The focus of this Climate Dialogue will be on the reliability of climate simulations for the regional scale. An important question will be if there is added value from regional climate downscaling.
More specific questions:
1) How realistic are simulations by GCM’s on the regional scale?
2) Do some parameters (e.g. temperature) perform better than others (e.g. precipitation)?
3) Do some regions perform better than others?
4) To what extent can regional climate models simulate the past?
5) What is the best way to determine the skill of the hind cast?
6) Is there added value of regional models in comparison with global models?
7) What are the relative merits of dynamical and statistical downscaling?
8) How should one judge projections of these regional models?
9) Should global/regional climate models be used for decisions concerning infrastructure development? If so how? If not, what should form a better scientific base for such decisions?
 The CMIP3 and CMIP5 list of publications is a good starting point, see http://www-pcmdi.llnl.gov/ipcc/subproject_publications.php
 G.J. van Oldenborgh, F.J. Doblas Reyes, S.S. Drijfhout, and E. Hawkins, "Reliability of regional climate model trends", Environmental Research Letters, vol. 8, pp. 014055, 2013. http://dx.doi.org/10.1088/1748-9326/8/1/014055
 Anagnostopoulos, G. G., Koutsoyiannis, D., Christofides, A., Efstratiadis, A. &Mamassis, N. (2010) A comparison of local and aggregated climate model outputs with observed data. Hydrol. Sci. J. 55(7), 1094–1110
 Stephens, G. L., T. L’Ecuyer, R. Forbes, A. Gettlemen, J.‐C. Golaz, A. Bodas‐Salcedo, K. Suzuki, P. Gabriel, and J. Haynes (2010), Dreary state of precipitation in global models, J. Geophys. Res., 115, D24211, doi:10.1029/2010JD014532
 J. Bhend, and P. Whetton, "Consistency of simulated and observed regional changes in temperature, sea level pressure and precipitation", Climatic Change, 2013. http://dx.doi.org/10.1007/s10584-012-0691-2
 Wilby, R. L. (2010) Evaluating climate model outputs for hydrologicalapplications – Opinion. Hydrol. Sci. J. 55(7), 1090–1093
 Kundzewicz, Zbigniew W. and Stakhiv, Eugene Z.(2010) 'Are climate models “ready for prime time” inwater resources management applications, or is more research needed?', Hydrological Sciences Journal, 55: 7, 1085 —1089
 Pielke, R. A. S., and R. L. Wilby, 2012: Regional climate downscaling: What’s the point? Eos Trans.AGU, 93, PAGE 52, doi:201210.1029/2012EO050008
 R.A. Kerr, "Forecasting Regional Climate Change Flunks Its First Test", Science
, vol. 339, pp. 638-638, 2013. http://dx.doi.org/10.1126/science.339.6120.638
 Kundzewicz, Zbigniew W. and Stakhiv, Eugene Z.(2010) 'Are climate models “ready for prime time” in water resources management applications, or is more research needed?', Hydrological Sciences Journal, 55: 7, 1085 —1089
The added value of Regional Climate Models in climate change assessments
Regional downscaling of climate information is a popular activity in many applications addressing the assessment of possible effects of a systematic change of the climate characteristics at the local scale. Adding local information, not captured in the coarse scale climate model or observational archives, can provide an improved representation of the relevant processes at this scale, and thus yield additional information, for instance concerning topography, land use or small scale features such as sea breezes or organisation of convection. A necessary step in the application of tools used for this regional downscaling is a critical assessment of the quality of the tools: are regional climate models (RCMs), used for this climate information downscaling, good enough for this task?
It is important to distinguish the various types of analyses that are carried out with RCMs. And likewise to assess the ability of the RCM to perform the task that is assigned to them. And these types of analyses clearly cover a wider range than plain prediction of the local climate!
Regional climate prediction
Pielke and Wilby (2012) discuss the lack of potential of RCMs to increase the skill of climate predictions at the regional scale. Obviously, these RCM predictions heavily rely on the quality of the boundary conditions provided by global climate models, and fail to represent dynamically the spatial interaction between the region of interest and the rest of the world. However, various “big brother” type experiments (in which the ability of RCMs to reproduce a filtered signal provided by the boundary conditions (Denis et al, 2002), for instance carried out by colleagues at KNMI) do show that a high resolution regional model can add value to a coarse resolution boundary condition by improving the spatial structure of the projected mean temperatures. Also the spatial structure of changes in precipitation linked to altered surface temperature by convection can be improved by using higher resolution model experiments, although the relative gain here is generally small (Di Luca et al, 2012).
Van Oldenborgh et al (2013) point out that the spatial structure of the mean temperature trend in the recent CMIP5 model ensemble compares fairly well with observations, but anomalies from the mean temperature trend aren’t well captured. This uncertainty clearly limits the predictability of temperatures at the regional scale beyond the mean trend. Van Haren et al (2012) also nicely illustrate the dependence of regional skill on lateral boundary conditions: simulations of (historic) precipitation trends for Europe failed to match the observed trends when lateral boundary conditions were provided from an ensemble of CMIP3 global climate model simulations, while a much better correspondence with observations was obtained when reanalyses were used as boundary condition. Thus, a regional prediction of a trend can only be considered to be skilful when the boundary forcings represent the signal to be forecasted adequately. And this does apply to mean temperature trends for most places in the world, but not for anomalies from these mean trends, nor for precipitation projections.
For regional climate predictability, the added value of RCMs should come from better resolving the relationship between mean (temperature) trends and key indicators that are supposedly better represented in the high resolution projections utilizing additional local information, such as temperature or precipitation extremes. Also here, evidence of adding skill is not univocally demonstrated. Min et al (2013) evaluate the ability of RCMs driven by reanalysis data to reproduce observed trends in European annual maximum temperatures, and conclude that there is a clear tendency to underestimate the observed trends. For Southern Europe biases in maximum temperatures could be related to errors in the surface flux partitioning (Stegehuis et al, 2012), but no such relationship was found for NW Europe by Min et al (2013).
Thus indeed, the limitations to predictability or regional climate information by RCMs as discussed by Pielke and Wilby (2012) and others are valid, and care must be taken while interpreting RCM projections as predictive assessments. But is this the only application of RCMs? Not really. We will discuss two other applications, together with the degree to which limitations in RCM skill apply and are relevant.
Bottom up environmental assessments
A fair point of critique to exploring a cascade of model projections ranging from the global scale down to the local scale of a region of interest to developers of adaptation or mitigation policies is the virtually unmanageable increase of the range of degrees of freedom, also addressed as “uncertainty”. Uncertainty arises from imperfect models, inherent variability, and unknown evolution of external forcings. And in fact the process of (dynamical) downscaling adds another level of uncertainty, related to the choice of downscaling tools and methodologies. The reverse approach, starting from the vulnerability of a region or sector of interest to changes in environmental conditions (Pielke et al, 2012), does not eliminate all sources of uncertainty, but allows a focus on the relevant part of the spectrum, including those elements that are not related to greenhouse gas induced climate change.
But also here, RCMs can be of great help, not necessarily by providing reliable predictions, but also by supporting evidence about the salience of planned measures or policies (Berkhout et al, 2013). A nice example is a near flooding situation in Northern Netherlands (January 2012), caused by a combined occurrence of a saturated soil due to excessive antecedent precipitation, a heavy precipitation event in the coastal area and a storm surge with a duration of several days that hindered the discharge of excess water from the area. This is typically a “real weather” event that is not necessarily exceptional but does expose a local vulnerability to superfluous water. The question asked by the local water managers was whether the combination of the individual events (wet soil, heavy rain, storm surge) has a causal relationship, and whether the frequency of occurrence of compound events can be expected to change in the future. Observational analyses do suggest a link between heavy precipitation and storm surge, but the available dataset was too short to explore the statistical relationships in a relevant part of the frequency distribution. A large set of RCM simulations is now explored to increase the statistical sample, but – more importantly – to provide a physically comprehensive picture of the boundary conditions leading up to an event like this. By enabling the policy makers to communicate this physically comprehensive picture provides public support for measures undertaken to adapt to this kind of events. This exploration of model based – synthetic – future weather is a powerful method to assess the consequences of possible changes in regional climate variability for the local water management.
Apart from a tool to predict a system given its initial state and the boundary forcings on it, a model is a collection of our understanding of the system itself. Its usefulness is not limited to its ability to predict, but also to describe the dynamics of a system, governed by internal processes and interactions with its environment. Regional climate models should likewise be considered as “collections of our understanding of the regional climate system”. And can likewise be used to study this system, and learn about it. There are numerous studies where regional climate model studies have increased our understanding of the mechanism of the climate system acting on a regional scale. A couple of examples:
- Strong trends in coastal precipitation, and particularly a series of extreme precipitation events in the Netherlands, could successfully be attributed to anomalies in sea surface temperature (SST) in the nearby North Sea (Lenderink et al, 2009). Strong SST gradients close to the coast needed to be imposed to the RCM simulations carried out to reveal this mechanism. The relationship between SSTs and spatial gradients in changes in (extreme) precipitation is an important finding for analysing necessary measures to anticipate future changes in the spatial and temporal distribution of rainfall in the country.
- During the past century land use change has given rise to regional changes in the local surface climatology, particularly the mean and variability of near surface temperature (Pitman et al, 2012). A set of GCM simulations dedicated to quantify the effect of land use change relative to changes in the atmospheric greenhouse gas concentration over the past century revealed that the land use effect is largely limited to the area of land use change. Wramneby et al (2010) explored the regional interaction between climate and vegetation response using a RCM set-up, and highlighted the importance of this interaction for assessing the mean temperature response particularly at high latitudes (due to the role of vegetation in snow covered areas) and in water limited evaporation regimes (due to the role of vegetation in controlling surface evaporative cooling).
- In many occasions the degree to which anomalies in the land surface affect the overlying atmosphere depends on the resolved spatial scale. As an example, Hohenegger et al (2009) investigated the triggering of precipitation in response to soil moisture anomalies with a set of regional models ranging in physical formulation and resolution. This issue that deserves a lot of attention in the literature due to the possible existence of (positive) feedbacks that may affect occurrence or intensity of hydrological extremes such as heatwaves. In her study, RCMs operating at the typical 25 – 50 km scale resolution tend to overestimate the positive soil moisture – precipitation feedback in the Alpine area, which is better represented by higher resolution models. It is a study that points at a possible mechanism that needs to be adequately represented for generating reliable projections.
Each of these examples (and many more that can be cited) generates additional insight in the processes controlling local climate variability by allowing to zoom in on these processes using RCMs. They thus contribute to setting the research agenda in order to improve our understanding of drivers of regional change.
Climate predictions versus climate scenarios
The notion that a tool – an RCM – may possess shortcomings in its predictive skill, but simultaneously prove to be a valuable tool to support narratives that are relevant to policy making and spatial planning can in fact be extended to highlighting the difference between “climate predictions” and “climate scenarios”. Scenarios are typically used when deterministic or probabilistic predictions show too little skill to be useful, either because of the complexity of the considered system, or because of the fundamental limitations to its predictability (Berkhout et al, 2013). A scenario is a “what if” construction, a tool to create a mental map of the possible future conditions assuming a set of driving boundary conditions. For a scenario to be valuable it does not necessarily need to have predictive skill, although a range of scenarios can be and are being interpreted as a probability range for future conditions. A (single) scenario is mainly intended to feed someone’s imagination with a plausible, comprehensible and internally consistent picture. Used this way, also RCMs with limited predictive skill can be useful tools for scenario development and providing supporting narratives that generate public awareness or support for preparatory actions. For this, the RCM should be trustworthy in producing realistic and consistent patterns of regional climate variability, and abundant application, verification and improving is a necessary practice. Further developments of RCMs as a Regional Earth System Exploration tool, by linking the traditional meteorological models to hydrological, biogeophysical and socio-economic components, can further develop their usefulness in practice.
Bart van den Hurk has a PhD on land surface modelling, obtained in Wageningen in 1996. Since then he has worked at the Royal Netherlands Meteorological Institute (KNMI) as researcher, involved in studies addressing modelling land surface processes in regional and global climate models, data assimilation of soil moisture, and constructing regional climate change scenarios. He is strongly involved with the KNMI global modelling project EC-Earth, and is co-author of the land surface modules of the European Centre for Medium Range Weather Forecasts (ECMWF). Since 2005 he is part-time professor “Regional Climate Analysis” at the Institute of Marine and Atmospheric Research (IMAU) at the Utrecht University. There he teaches masters students, supervises PhD-students and is involved in several research networks. Between 2007 and 2010 he was chair of the WCRP-endorsed Global Land-Atmosphere System Studies (GLASS) panel, since 2006 member of the council of the Netherlands Climate Changes Spatial Planning program, and since 2008 member of the board of the division “Earth and Life Sciences” of the Dutch Research Council (NWO-ALW). He is convenor at a range of incidental and periodic conferences, and editor for Hydrology and Earth System Science (HESS).
Berkhout, F., B. van den Hurk, J. Bessembinder, J. de Boer, B. Bregman en M. van Drunen (2013), Framing climate uncertainty: using socio-economic and climate scenarios in assessing climate vulnerability and adaptation; submitted Regional and Environmental Change.
Denis, B., R. Laprise, D. Caya and J. Côté, 2002: Downscaling ability of one-way-nested regional climate models: The Big-Brother experiment. Clim. Dyn. 18, 627-646.
Di Luca, A., Elía, R. & Laprise, R., 2012. Potential for small scale added value of RCM’s downscaled climate change signal. Climate Dynamics. Available at: http://www.springerlink.com/index/10.1007/s00382-012-1415-z
Hohenegger, C., P. Brockhaus, C. S. Bretherton, C. Schär (2009): The soil-moisture precipitation feedback in simulations with explicit and parameterized convection, in: Journal of Climate 22, pp. 5003–5020.
Lenderink, G., E. van Meijgaard en F. Selten (2009), Intense coastal rainfall in the Netherlands in response to high sea surface temperatures: analysis of the event of August 2006 from the perspective of a changing climate; Clim. Dyn., 32, 19-33, doi:10.1007/s00382-008-0366-x.
Min, E., W. Hazeleger, G.J. van Oldenborgh en A. Sterl (2013), Evaluation of trends in high temperature extremes in North-Western Europe in regional climate models; Environmental Research Letters, 8, 1, 014011, doi:10.1088/1748-9326/8/1/014011.
Pielke, R. A. S., and R. L. Wilby, 2012: Regional climate downscaling: What’s the point? Eos Trans.AGU, 93, PAGE 52, doi:201210.1029/2012EO050008
Pielke, R. A., Sr., R. Wilby, D. Niyogi, F. Hossain, K. Dairuku,J. Adegoke, G. Kallos, T. Seastedt, and K. Suding (2012), Dealing with complexity and extreme events using a bottom-up, resource-based vulnerability perspective, in Extreme Events and Natural Hazards: The Complexity Perspective, Geophys. Monogr. Ser., vol. 196, edited by A. S. Sharma et al. 345–359, AGU, Washington, D. C., doi:10.1029/2011GM001086.
Pitman, A., N. de Noblet, F. Avila, L. Alexander, J.P. Boissier, V. Brovkin, C. Delire, F. Cruz, M.G. Donat, V. Gayler, B.J.J.M. van den Hurk, C. Reick en A. Voldoire (2012): Effects of land cover change on temperature and rainfall extremes in multi-model ensemble simulations; Earth System Dynamics, 3, 213-231, doi:10.5194/esd-3-213-2012.
Stegehuis A., Vautard R., Teuling R., Ciais P. Jung M. Yiou, P. (2012) Summer temperatures in Europe and land heat fluxes in observation-based data and regional climate model simulations, in press by Climate Dynamics; doi 10.1007/s00382-012-1559-x
Van Haren, R. G.J. van Oldenborgh, G. Lenderink, M. Collins en W. Hazeleger (2012), SST and circulation trend biases cause an underestimation of European precipitation trends; Clim. Dyn., doi:10.1007/s00382-012-1401-5.
Van Oldenborgh, G.J. F.J. Doblas-Reyes, S.S. Drijfhout en E. Hawkins (2013), Reliability of regional climate model trends; Environmental Research Letters, 8, 1, 014055, doi:10.1088/1748-9326/8/1/014055.
Wramneby, A., B. Smith, and P. Samuelsson (2010), Hot spots of vegetation-climate feedbacks under future greenhouse forcing in Europe, J. Geophys. Res., 115, D21119, doi:10.1029/2010JD014307.
Are climate models ready to make regional projections?
Global Climate Models (GCMs) are designed to provide insight into the global climate system. They have been used to investigate the impacts of changes in various climate system forcings such as volcanoes, solar radiation, and greenhouse gases, and have proved themselves to be useful tools in this respect. The growing interest in GCM performance at regional scales, rather than global, has come from at least two different directions: the climate modelling community and the climate change adaptation community.
Due, in part, to the ever increasing computational power available, GCMs are being continually developed and applied at higher spatial resolutions. Many GCM modelling groups have been increasing the resolution from ~250km grid boxes 7 years ago to ~100km grid boxes today. This model resolution increase leads naturally to model development and evaluation exercises that pay closer attention to smaller scales, in this case, regional instead of global scales. The Fifth Coupled Model Intercomparison Experiment (CMIP5) provides a large ensemble of GCM simulations, many of which are at resolutions high enough to warrant evaluation at regional scales. Over the next few years these GCM simulations will be extensively evaluated, problems will be found (as seen in some early evaluations1,2), followed hopefully by solutions that lead to further model development and improved simulations. This step of finding a solution to an identified problem is the hardest in the model development cycle, and I applaud those who do it successfully.
Probably the stronger demand for regional scale information from climate models is coming from the climate change adaptation community. Given only modest progress in climate change mitigation, adaptation to future climate change is required. Some sectors, such as those involved in large water resource projects (e.g. building a new dam), are particularly vulnerable to climate change. They are planning to invest large amounts of money (millions) in infrastructure, with planned lifetimes of 50-100 years, that directly depend on climate to be successful. Over such long lifetimes, greenhouse gas driven climate change is expected to increase temperature by a few degrees, and may cause significant changes in precipitation, depending on the location. Many of the systems required to adapt are more sensitive to precipitation than temperature, and projections of precipitation often have considerably more uncertainty associated with them. The question for the climate change adaptation community is whether the uncertainty (including model errors) in the projected climate change is small enough to be useful in a decision making framework.
From a GCM perspective then, the answer to “Are climate models ready to make regional projections?” is two-fold. For the climate modelling community the answer is yes. GCMs are being run at high enough resolution to make regional scale (so long as your regions are many 100kms across) evaluations and projections useful to inform the model development and hopefully improve future simulations. For the climate change adaptation community, whose spatial scale of interest is often much lower than current high resolution GCMs can capture, the answer in general is no. The errors in the simulated regional climate and the inter-model uncertainty in regional climate projections from GCMs is often too large to be useful in decision making. These climate change adaptation decisions need to be made however, and in an effort to produce useful regional scale climate information that embodies the global climate change a number of “downscaling” techniques have been developed.
It is worth noting that some climate variables, such as temperature, tend to be simulated better by climate models than other variables, such as precipitation. This is at least partly due to the scales and non-linearity of physical processes which effect each variable. This is demonstrated in the fourth IPCC report which mapped the level of agreement in the sign of the change in precipitation projected by GCMs. This map showed large parts of the world where GCMs disagreed about the sign of the change in precipitation. However this vastly underestimated the agreement between the GCMs3. They showed that much of this area of disagreement is actually areas where the GCMs agree that the change will be small (or zero). That is, if the actual projected change is zero then by chance, some GCMs will project small increases and some small decreases. This does not indicate disagreement between the models, rather they all agree that the change is small.
Regional climate models
Before describing the downscaling techniques, it may be useful to consider this question: What climate processes, that are important at regional scales, may be missing in GCMs?
The first set of processes relates directly to how well resolved land surface features such as mountains and coastlines are. Mountains cause local deviations in low level air flow. When air is forced to rise to get over a mountain range it can trigger precipitation, and because of this, mountains are often a primary region for the supply of fresh water resources. At GCM resolution mountains are often under-represented in terms of height or spatial extent and so do not accurately capture this relationship with precipitation. In fact, some regionally important mountain ranges, such as the eastern Mediterranean coastal range or the Flinders Range in South Australia, are too small to be represented at all in some GCMs. Using higher spatial resolution to better resolve the mountains should improve the models ability to capture this mountain-precipitation relationship.
Similarly, higher resolution allows model coastlines to be closer to the location of actual coastlines, and improves the ability to capture climate processes such as sea breezes.
The second set of processes are slightly more indirect and often involve an increase in the vertical resolution as well. These processes include the daily evolution of the planetary boundary layer, and the development of low level and mountain barrier jets.
A simple rule of thumb is that one can expect downscaling to higher resolution to improve the simulation of regional climate in locations that include coastlines and/or mountain ranges (particularly where the range is too small to be well resolved by the GCM but large enough to be well resolved at the higher resolution) while not making much difference over large homogeneous, relatively flat regions (deserts, oceans,...).
So, there are physical reasons one might expect downscaling to higher resolution will improve the simulation of regional climate. How do we go about this downscaling?
Downscaling techniques can generally be divided into two types: statistical and dynamical. Statistical techniques generally use a mathematical method to form a relationship between the modelled climate and observed climate at an observation station. A wide variety of mathematical methods can be used but they all have two major limitations. First, they rely on long historical observational records to calculate the statistical relationship, effectively limiting the variables that can be downscaled to temperature and precipitation, and the locations to those stations where these long records were collected. Second, they assume that the derived statistical relationship will not change due to climate change.
Dynamical downscaling, or the use of Regional Climate Models (RCMs), does not share the limitations of statistical downscaling. The major limitation in dynamical downscaling is the computational cost of running the RCMs. This generally places a limit on both the spatial resolution (often 10s of kilometres) and the number of simulations that can be performed to characterise uncertainty. RCMs also contain biases, both inherited from the driving GCM and generated with the RCM itself. It is worth noting that statistical downscaling techniques can be applied to RCM simulations as easily as GCM simulations to obtain projections at station locations.
The RCM limitations are actively being addressed through current initiatives and research. Like GCMs, RCMs benefit from the continued increase in computational power, allowing more simulations to be run at higher spatial resolution. The need for more simulations to characterise uncertainty is being further addressed through international initiatives to have many modelling groups contribute simulations to the same ensembles (e.g. CORDEX - COordinated Regional climate Downscaling EXperiment http://wcrp-cordex.ipsl.jussieu.fr/). New research into model independence is also pointing toward ways to create more statistically robust ensembles4. Novel research to reduce (or eliminate) the bias inherited from the driving GCM is also showing promise5,6.
Above I used simple physical considerations to suggest there would be some added value from regional models compared to global models. Others have investigated this from an observational viewpoint7,8 as well as through direct evaluation of model results at different scales9,10,11. In each case the results agreed with the rule of thumb given earlier. That is, in the areas with strong enough regional climate influences we do see improved simulations of regional climate at higher resolutions. Of course we are yet to address the question of whether the regional climate projections from these models have low enough uncertainty to be useful in climate change adaptation decision making.
To date RCMs have been used in many studies12,13 and a wide variety of evaluations of RCM simulations against observations have been performed. In attempting to examine the fidelity of the regional climate simulated, a variety of variables (temperature, precipitation, wind, surface pressure,...) have been evaluated using a variety of metrics14,15, with all the derived metrics then being combined to produce an overall measure of performance. When comprehensive assessments such as these are performed it is often found that different models have different strengths and weaknesses as measured by the different metrics. If one has a specific purpose in mind, e.g. building a new dam, one may wish to focus on metrics directly relevant to that purpose. Often the projected climate change is of interest so the evaluation should include a measure of the models ability to simulate change, often given as a trend over a recent historical period16,17. In most cases the RCMs are found to do a reasonably good job of simulating the climate of the recent past. Though, there are usually places and/or times where the simulation is not very good. Not surprising for an active area of research and model development.
Given the evaluation results found to date it is advisable to carefully evaluate each RCMs simulations before using any climate projections they produce. Being aware of where and when they perform well (or poorly) is important when assessing the climate change that model projects. It is also preferable for the projections themselves to be examined with the aim of understanding the physical mechanisms causing the projected changes. Good process level understanding of the causes behind the changes provides another mechanism through which to judge their veracity.
Ready for adaptation decisions?
Finally we come to the question of whether regional climate projections should be used in climate change adaptation decisions concerning infrastructure development? In the past such decision were made assuming a stationary climate such that observations of the past were representative of the future climate. So the real question here is will the use of regional climate projections improve decisions made when compared to the use of historical climate observations?
If the projected regional change is large enough that it falls outside the historical record even when considering the associated model errors and uncertainties, then it may indeed impact the decision. Such decisions are made within a framework that must consider uncertainty in many factors other than climate including future economic, technological and demographic pathways. Within such a framework, risk assessments are performed to inform the decision making process, and the regional climate projections may introduce a risk to consider that is not present in the historical climate record. If this leads to decisions which are more robust to future climate changes (as well as demographic and economic changes) then it is worthwhile including the regional climate projections in the decision making process.
Of course, this relies on the uncertainty in the regional climate projection being small enough for the information to be useful in a risk assessment process. Based on current models, this is not the case everywhere, and continued model development and improvement is required to decrease the uncertainty and increase the utility of regional climate projections for adaptation decision making.
Jason Evans has a B. Science (Physics) and a B. Math (hons) from the University of Newcastle, Australia. He has a Ph.D. in hydrology and climatology from the Australian National University. He worked for several years as a research associate at Yale University, USA, before moving to the University of New South Wales, Sydney, Australia. He is currently an Associate Professor in the Climate Change Research Centre there. His research involves general issues of regional climate and water cycle processes over land. He focuses at the regional (or watershed) scale and studies processes including river flow, evaporation/transpiration, water vapour transport and precipitation. He is currently Co-Chair of the GEWEX Regional Hydroclimate Panel, and coordinator of the Coordinated Regional Climate Downscaling Experiment (CORDEX), both elements of the World Climate Research Programme.
 van Oldenborgh GJ, Doblas Reyes FJ, Drijfhout SS, Hawkins E (2013) Reliability of regional climate model trends. Environmental Research Letters 8
 Bhend J, Whetton P (2013) Consistency of simulated and observed regional changes in temperature, sea level pressure and precipitation.
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Are climate models ready to make regional projections?
The question that is addressed in my post is, with respect to multi-decadal model simulations, are global and/or regional climate models ready to be used for skillful regional projections by the impacts and policymaker communities?
This could also be asked as
Are skillful (value-added) regional and local multi-decadal predictions of changes in climate statistics for use by the water resource, food, energy, human health and ecosystem impact communities available at present?
As summarized in this post, the answer is NO.
In fact, the output of these models are routinely being provided to the impact communities and policymakers as robust scientific results, when, in fact, they only provide an illusion of skill. Simply plotting high spatial resolution model results is not, by itself, a skillful product!
Skill is defined as accurately predicting changes in climate statistics on this time period. This skill must be assessed by predicting global, regional and local average climate, and any climate change that was observed over the last several decades (i.e. “hindcast model predictions”).
One issue that we need to make sure is clearly understood are the terms “prediction” and “projection”. The term “projection”, of course, is just another word for a “prediction” when specified forcings are prescribed; e.g. such as different CO2 emission scenarios - see Pielke (2002). Thus “projection” and “prediction” are synonyms.
Dynamic and statistical downscaling is widely used to refine predictions from global climate models to smaller spatial scales. In order to classify the types of dynamic and statistical downscaling, Castro et al (2005) defined four categories. These are summarized in Pielke and Wilby (2012) and in Table 1 from that paper.
In the current post, I am referring specifically to Type 4 downscaling. For completeness, I list below all types of downscaling. The intent of downscaling is to achieve accurate, higher spatial resolution of weather and other components of the climate system than is achievable with the coarser spatial resolution global model. Indeed, one test of a downscaled result is whether its results agree more with observations than does the global model results simply downscaled by interpolation to a finer terrain and landscape map.
Types of Downscaling
Type 1 downscaling is used for short-term, numerical weather prediction. In dynamic Type 1 downscaling, the regional model includes initial conditions from observations. In Type 1 statistical downscaling the regression relationships are developed from observed data and the Type 1 dynamic model predictions.
The Type 1 application of downscaling is operationally used by weather services worldwide. They provide very significant added regional and local prediction skill beyond what is available from the parent global model (e.g. see http://weather.rap.ucar.edu/model/). Millions of Type 1 forecasts are made every year and verified with real world weather, thus providing an opportunity for extensive quantitative testing of their predictive skill (Pielke Jr. 2010) .
Type 2 dynamic downscaling refers to regional weather (or climate) simulations in which the regional model’s initial atmospheric conditions are forgotten (i.e., the predictions do not depend on the specific initial conditions), but results still depend on the lateral boundary conditions from a global numerical weather prediction where initial observed atmospheric conditions are not yet forgotten, or are from a global reanalysis. Type 2 statistical downscaling uses the regression relationships developed for Type 1 statistical downscaling except that the input variables are from the Type 2 weather (or climate) simulation. Downscaling from reanalysis products (Type 2 downscaling) defines the maximum forecast skill that is achievable with Type 3 and Type 4 downscaling.
Type 2 downscaling provides an effective way to provide increased value-added information on regional and local spatial scales. It is important to recognize, however, that type 2 downscaling is not a prediction (projection) for the cases where the global data comes from a reanalysis (e.g. a reanalysis is a combination of real world observations, folded into a global model). An example of this type of application is reported in Feser et al., 2011, Pielke 2013 and Mearns et al 2012, 2013b).
When Type 2 results are presented to the impacts communities as a valid analysis of the skill of Type 4 downscaling, those communities are being misled on the actual robustness of the results in terms of multi-decadal projections. Type 2 results, even from global models used in a prediction mode, still retain real world information in the atmosphere (such as from long wave jet stream patterns), as well as sea surface temperatures, deep soil moisture, and other climate variables that have long term persistence.
Type 3 dynamic downscaling takes lateral boundary conditions from a global model prediction forced by specified real world surface boundary conditions, such as for seasonal weather predictions based on observed sea surface temperatures, but the initial observed atmospheric conditions in the global model are forgotten. Type 3 statistical downscaling uses the regression relationships developed for Type 1 statistical downscaling, except using the variables from the global model prediction forced by specified real-world surface boundary conditions.
Type 3 downscaling is applied, for example, for seasonal forecasts where slowly changing anomalies in the surface forcing (such as sea surface temperature) provide real-world information to constrain the downscaling results. Examples of the level of limited, but non-zero, skill achievable are given in Castro et al (2007) and Veljovic et al (2012).
Type 4 dynamic downscaling takes lateral boundary conditions from an Earth system model in which coupled interactions among the atmosphere, ocean, biosphere, and cryosphere are predicted [e.g., Solomon et al., 2007]. Other than terrain, all other components of the climate system are calculated by the model except for human forcings, including greenhouse gas emissions scenarios, which are prescribed. Type 4 dynamic downscaling is widely used to provide policy makers with impacts from climate decades into the future. Type 4 statistical downscaling uses transfer functions developed for the present climate, fed with large scale atmospheric information taken from Earth system models representing future climate conditions. It is assumed that statistical relationships between real-world surface observations and large scale weather patterns will not change.
The level of skill achievable deteriorates from Type 1 to Type 2 to Type downscaling, as fewer observations are used to constrain the model realism. For Type 4, except for the prescribed forcing (such as added CO2), there are no real world constraints.
It is also important to realize that the models, while including aspects of basic physics (such as the pressure gradient force, advection, gravity), are actually engineering code. All of the parameterizations of the physics, chemistry and biology include tunable parameters and functions. For the atmospheric part of climate models, this engineering aspect of weather models is discussed in depth in Pielke (2013a). Thus, such engineering (parameterized) components can result in the drift of the model results away from reality when observations are not there to constrain this divergence from reality. The climate models are not basic physics.
There are two critical tests for skillful Type 4 model runs:
1. The model must provide accurate replications of the current climatic conditions on the global, regional and local scale? This means the model must be able to accurately predict the statistics of the current climate. This test, of course, needs to be performed in a hindcast mode?
2. The model must also provide accurate predictions of changes in climatic conditions (i.e. the climatic statistics) on the regional and local scale? This means the model must be able to replicate the changes in climate statistics over this time period. This can also, of course, only be assessed by running the models in a hindcast mode.
For Type 4 runs [i.e. multidecadal projections], the models being used include the CMIP5 projections.
As reported in The CMIP5 - Coupled Model Intercomparison Project Phase 5
CMIP5 promotes a standard set of model simulations in order to:
· evaluate how realistic the models are in simulating the recent past,
· provide projections of future climate change on two time scales, near term (out to about 2035) and long term (out to 2100 and beyond), and
· understand some of the factors responsible for differences in model projections, including quantifying some key feedbacks such as those involving clouds and the carbon cycle
The CMIP5 runs, unfortunately, perform poorly with respect to the first bullet listed above, as documented below.
If the models’ are not sufficiently realistic in simulating the climate in the recent past, they are not ready to be used to provide projections for the coming decades!
A number of examples from the peer reviewed literature illustrate this inadequate performance.
Summary of a Subset of Peer-Reviewed Papers That Document the Limitations of the CMIP5 model projections with respect to Criteria #1.
· Taylor et al, 2012: Afternoon rain more likely over drier soils. Nature. doi:10.1038/nature11377. Received 19 March 2012 Accepted 29 June 2012 Published online 12 September 2012
“…the erroneous sensitivity of convection schemes demonstrated here is likely to contribute to a tendency for large-scale models to `lock-in’ dry conditions, extending droughts unrealistically, and potentially exaggerating the role of soil moisture feedbacks in the climate system.”
· Driscoll, S., A. Bozzo, L. J. Gray, A. Robock, and G. Stenchikov (2012), Coupled Model Intercomparison Project 5 (CMIP5) simulations of climate following volcanic eruptions, J. Geophys. Res., 117, D17105, doi:10.1029/2012JD017607. published 6 September 2012.
The study confirms previous similar evaluations and raises concern for the ability of current climate models to simulate the response of a major mode of global circulation variability to external forcings.
· Fyfe, J. C., W. J. Merryfield, V. Kharin, G. J. Boer, W.-S. Lee, and K. von Salzen (2011), Skillful predictions of decadal trends in global mean surface temperature, Geophys. Res. Lett.,38, L22801, doi:10.1029/2011GL049508
”….for longer term decadal hindcasts a linear trend correction may be required if the model does not reproduce long-term trends. For this reason, we correct for systematic long-term trend biases.”
· Xu, Zhongfeng and Zong-Liang Yang, 2012: An improved dynamical downscaling method with GCM bias corrections and its validation with 30 years of climate simulations. Journal of Climate 2012 doi: http://dx.doi.org/10.1175/JCLI-D-12-00005.1
”…the traditional dynamic downscaling (TDD) [i.e. without tuning) overestimates precipitation by 0.5-1.5 mm d-1.....The 2-year return level of summer daily maximum temperature simulated by the TDD is underestimated by 2-6°C over the central United States-Canada region".
· Anagnostopoulos, G. G., Koutsoyiannis, D., Christofides, A., Efstratiadis, A. & Mamassis, N. (2010) A comparison of local and aggregated climate model outputs with observed data. Hydrol. Sci. J. 55(7), 1094–1110
".... local projections do not correlate well with observed measurements. Furthermore, we found that the correlation at a large spatial scale, i.e. the contiguous USA, is worse than at the local scale."
· Stephens, G. L., T. L’Ecuyer, R. Forbes, A. Gettlemen, J.‐C. Golaz, A. Bodas‐Salcedo, K. Suzuki, P. Gabriel, and J. Haynes (2010), Dreary state of precipitation in global models, J. Geophys. Res., 115, D24211, doi:10.1029/2010JD014532.
"...models produce precipitation approximately twice as often as that observed and make rainfall far too lightly.....The differences in the character of model precipitation are systemic and have a number of important implications for modeling the coupled Earth system .......little skill in precipitation [is] calculated at individual grid points, and thus applications involving downscaling of grid point precipitation to yet even finer‐scale resolution has little foundation and relevance to the real Earth system.”
· Sun, Z., J. Liu, X. Zeng, and H. Liang (2012), Parameterization of instantaneous global horizontal irradiance at the surface. Part II: Cloudy-sky component, J. Geophys. Res., doi:10.1029/2012JD017557, in press.
“Radiation calculations in global numerical weather prediction (NWP) and climate models are usually performed in 3-hourly time intervals in order to reduce the computational cost. This treatment can lead to an incorrect Global Horizontal Irradiance (GHI) at the Earth’s surface, which could be one of the error sources in modelled convection and precipitation. …… An important application of the scheme is in global climate models….It is found that these errors are very large, exceeding 800 W m-2 at many non-radiation time steps due to ignoring the effects of clouds….”
· Ronald van Haren, Geert Jan van Oldenborgh, Geert Lenderink, Matthew Collins and Wilco Hazeleger, 2012: SST and circulation trend biases cause an underestimation of European precipitation trends Climate Dynamics 2012, DOI: 10.1007/s00382-012-1401-5
“To conclude, modeled atmospheric circulation and SST trends over the past century are significantly different from the observed ones. These mismatches are responsible for a large part of the misrepresentation of precipitation trends in climate models. The causes of the large trends in atmospheric circulation and summer SST are not known.”
· Driscoll, S., A. Bozzo, L. J. Gray, A. Robock, and G. Stenchikov (2012), Coupled Model Intercomparison Project 5 (CMIP5) simulations of climate following volcanic eruptions, J. Geophys. Res., 117, D17105, doi:10.1029/2012JD017607. published 6 September 2012.
The models generally fail to capture the NH dynamical response following eruptions. ……The study confirms previous similar evaluations and raises concern for the ability of current climate models to simulate the response of a major mode of global circulation variability to external forcings
· Mauritsen, T., et al. (2012), Tuning the climate of a global model, J. Adv. Model. Earth Syst., 4, M00A01, doi:10.1029/2012MS000154. published 7 August 2012
During a development stage global climate models have their properties adjusted or tuned in various ways to best match the known state of the Earth’s climate system…..The tuning is typically performed by adjusting uncertain, or even non-observable, parameters related to processes not explicitly represented at the model grid resolution.
· Jiang, J. H., et al. (2012), Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A-Train” satellite observations, J. Geophys. Res., 117, D14105, doi:10.1029/2011JD017237. published 18 July 2012.
The modeled mean CWCs [cloud water] over tropical oceans range from ∼3% to ∼15× of the observations in the UT and 40% to 2× of the observations in the L/MT. For modeled H2Os, the mean values over tropical oceans range from ∼1% to 2× of the observations in the UT and within 10% of the observations in the L/MT….Tropopause layer water vapor is poorly simulated with respect to observations. This likely results from temperature biases
· Van Oldenborgh, G.J., F.J. Doblas-Reyes, B. Wouters, W. Hazeleger (2012): Decadal prediction skill in a multi-model ensemble. Clim.Dyn. doi:10.1007/s00382-012-1313-4
who report quite limited predictive skill in two regions of the oceans on the decadal time period, but no regional skill elsewhere, when they conclude that "A 4-model 12-member ensemble of 10-yr hindcasts has been analysed for skill in SST, 2m temperature and precipitation. The main source of skill in temperature is the trend, which is primarily forced by greenhouse gases and aerosols. This trend contributes almost everywhere to the skill. Variation in the global mean temperature around the trend do not have any skill beyond the first year. However, regionally there appears to be skill beyond the trend in the two areas of well-known low-frequency variability: SST in parts of the North Atlantic and Pacific Oceans is predicted better than persistence. A comparison with the CMIP3 ensemble shows that the skill in the northern North Atlantic and eastern Pacific is most likely due to the initialisation, whereas the skill in the subtropical North Atlantic and western North Pacific are probably due to the forcing."
· Sakaguchi, K., X. Zeng, and M. A. Brunke (2012), The hindcast skill of the CMIP ensembles for the surface air temperature trend, J. Geophys. Res., 117, D16113, doi:10.1029/2012JD017765. published 28 August 2012
the skill for the regional (5° × 5° – 20° × 20° grid) and decadal (10 – ∼30-year trends) scales is rather limited…. The mean bias and ensemble spread relative to the observed variability, which are crucial to the reliability of the ensemble distribution, are not necessarily improved with increasing scales and may impact probabilistic predictions more at longer temporal scales.
· Kundzewicz, Z. W., and E.Z. Stakhiv (2010) Are climate models “ready for prime time” in water resources managementapplications, or is more research needed? Editorial. Hydrol. Sci. J. 55(7), 1085–1089.
who conclude that “Simply put, the current suite of climate models were not developed to provide the level of accuracy required for adaptation-type analysis.”
Even on the global average scale, the multi-decadal global climate models are performing poorly since 1998, as very effectively shown in an analysis by John Christy in the post by Roy Spencer which is reproduced below. As reported in Roy’s post, these plots by John are based upon data from the KNMI Climate Explorer with a comparison of 44 climate models versus the UAH and RSS satellite observations for global lower tropospheric temperature variations, for the period 1979-2012 from the satellites, and for 1975 – 2025 for the models.
Thus the necessary criteria #1 is not satisfied. Obviously, the first criteria must be satisfactorily addressed before one can have any confidence in the second criteria of claiming to skillfully predict changes in climate statistics.
To summarize the current state of modeling and the use of regional models to downscale for multi-decadal projections, as reported in Pielke and Wilby (2012):
1. The multi-decadal global climate model projection must include all first-order climate forcings and feedbacks, which, unfortunately, they do not.
2. Current global multi-decadal predictions are unable to skillfully simulate regional forcing by major atmospheric circulation features such as from El Niño and La Niña and the South Asian monsoon , much less changes in the statistics of these climate features. These features play a major role of climate impacts at the regional and local scales.
3. While regional climate downscaling yields higher spatial resolution, the downscaling is strongly dependent on the lateral boundary conditions and the methods used to constrain the regional climate model variables to the coarser spatial scale information from the parent global models. Large-scale climate errors in the global models are retained and could even be amplified by the higher-spatial- resolution regional models. If the global multi-decadal climate model predictions do not accurately predict large-scale circulation features, for instance, they cannot provide accurate lateral boundary conditions and interior nudging to regional climate models. The presence of higher spatial resolution information in the regional models, beyond what can be accomplished by interpolation of the global model output to a finer grid mesh, is only an illusion of added skill.
4. Apart from variable grid approaches, regional models do not have the domain scale (or two-way interaction between the regional and global models) to improve predictions of the larger-scale atmospheric features. This means that if the regional model significantly alters the atmospheric and/or ocean circulations, there is no way for this information to affect larger scale circulation features that are being fed into the regional model through the lateral boundary conditions and nudging.
5. The lateral boundary conditions for input to regional downscaling require regional-scale information from a global forecast model. However the global model does not have this regional-scale information due to its limited spatial resolution. This is, however, a logical paradox because the regional model needs something that can be acquired only by a regional model (or regional observations). Therefore, the acquisition of lateral boundary conditions with the needed spatial resolution becomes logically impossible. Thus, even with the higher resolution analyses of terrain and land use in the regional domain, the errors and uncertainty from the larger model still persist, rendering the added simulated spatial details inaccurate.
6. There is also an assumption that although global climate models cannot predict future climate change as an initial value problem, they can predict future climate statistics as a boundary value problem [Palmer et al., 2008]. However, for regional downscaling (and global) models to add value (beyond what is available to the impacts community via the historical, recent paleorecord and a worst case sequence of days), they must be able to skillfully predict changes in regional weather statistics in response to human climate forcings. This is a greater challenge than even skillfully simulating current weather statistics.
It is therefore inappropriate to present type 4 results to the impacts community as reflecting more than a subset of possible (plausible) future climate risks. As I wrote in Pielke (2011) with respect to providing multi-decadal climate predictions to the impacts and policy communities, there is a
“serious risk of overselling what [can be] provide[d] to policy makers. A significant fraction of the funds they are seeking for prediction could more effectively be used if they were spent on assessing risk and ways to reduce the vulnerability of local/regional resources to climate variability and change and other environmental issues using the bottom-up, resources-based perspective discussed in Pielke and Bravo de Guenni (2004), Pielke (2004), and Pielke et al. (2009). This bottom-up focus is “of critical interest to society.”
We wrote this recommendation also in Pielke and Wilby (2012):
As a more robust approach, we favor a bottom-up, resource-based vulnerability approach to assess the climate and other environmental and societal threats to critical assets [Wilby and Dessai, 2010; Kabat et al., 2004]. This framework considers the coping conditions and critical thresholds of natural and human environments beyond which external pressures (including climate change) cause harm to water resources, food, energy, human health, and ecosystem function. Such an approach could assist policy makers in developing more holistic mitigation and adaptation strategies that deal with the complex spectrum of social and environmental drivers over coming decades, beyond carbon dioxide and a few other greenhouse gases.
This is a more robust way of assessing risks, including from climate, than using the approach adopted by the Intergovernmental Panel on Climate Change (IPCC) which is primarily based on downscaling from multi-decadal global climate model projections. A vulnerability assessment using the bottom-up, resource-based framework is a more inclusive approach for policy makers to adopt effective mitigation and adaptation methodologies to deal with the complexity of the spectrum of social and environmental extreme events that will occur in the coming decades as the range of threats are assessed, beyond just the focus on CO2 and a few other greenhouse gases as emphasized in the IPCC assessments.
This need to develop a broader approach was even endorsed in the climate research assessment and recommendations in the “Report Of The 2004-2009 Research Review Of The Royal Netherlands Meteorological Institute”.
In this 2011 report, we wrote
The generation of climate scenarios for plausible future risk, should be significantly broadened in approach as the current approach assesses only a limited subset of possible future climate conditions. To broaden the approach of estimating plausible changes in climate conditions in the framing of future risk, we recommend a bottom-up, resource-based vulnerability assessment for the key resources of water, food, energy, human health and ecosystem function for the Netherlands. This contextual vulnerability concept requires the determination of the major threats to these resources from climate, but also from other social and environmental issues. After these threats are identified for each resource, then the relative risk from natural- and human-caused climate change (estimated from the global climate model projections, but also the historical, paleo-record and worst case sequences of events) can be compared with other risks in order to adopt the optimal mitigation/adaptation strategy.
Since the 2011 report (which I was a member of the Committee that wrote it), I now feel that using the global climate model projections, downscaled or not, to provide regional and local impact assessment on multi-decadal time scales is not an effective use of money and other resources. If the models cannot even accurately simulate current climate statistics when they are not constrained by real world data, the expense to run them to produce detailed spatial maps is not worthwhile. Indeed, it is counterproductive as it provides the impact community and policymakers with an erroneous impression on their value.
A robust approach is to use historical, paleo-record and worst case sequences of climate events. Added to this list can be perturbation scenarios that start with regional reanalysis (e.g. such as by arbitrarily adding a 1C increase in minimum temperature in the winter, a 10 day increase in the growing season, a doubling of major hurricane landfalls on the Florida coast, etc). There is no need to run the multi-decadal global and regional climate projections to achieve these realistic (plausible) scenarios.
Hopefully, our debate on this weblog will foster a movement away from the overselling of multi-decadal climate model projections to the impact and policy communities. I very much appreciate the opportunity to present my viewpoint in this venue.
Roger A. Pielke Sr. is currently a Senior Research Scientist in CIRES and a Senior Research Associate at the University of Colorado-Boulder in the Department of Atmospheric and Oceanic Sciences (ATOC) at the University of Colorado in Boulder (November 2005 -present). He is also an Emeritus Professor of Atmospheric Science at Colorado State University and has a five-year appointment (April 2007 - March 2012) on the Graduate Faculty of Purdue University in West Lafayette, Indiana.
Pielke has studied terrain-induced mesoscale systems, including the development of a three-dimensional mesoscale model of the sea breeze, for which he received the NOAA Distinguished Authorship Award for 1974. Dr. Pielke has worked for NOAA's Experimental Meteorology Lab (1971-1974), The University of Virginia (1974-1981), and Colorado State University (1981-2006). He served as Colorado State Climatologist from 1999-2006. He was an adjunct faculty member in the Department of Civil and Environmental Engineering at Duke University in Durham, North Carolina (July 2003-2006). He was a visiting Professor in the Department of Atmospheric Sciences at the University of Arizona from October to December 2004.
Roger Pielke Sr. was elected a Fellow of the AMS in 1982 and a Fellow of the American Geophysical Union in 2004. From 1993-1996, he served as Editor-in-Chief of the US National Science Report to the IUGG (1991-1994) for the American Geophysical Union. From January 1996 to December 2000, he served as Co-Chief Editor of the Journal of Atmospheric Science. In 1998, he received NOAA's ERL Outstanding Scientific Paper (with Conrad Ziegler and Tsengdar Lee) for a modeling study of the convective dryline. He was designated a Pennsylvania State Centennial Fellow in 1996, and named the Pennsylvania State College of Earth and Mineral Sciences Alumni of the year for 1999 (with Bill Cotton). He is currently serving on the AGU Focus Group on Natural Hazards (August 2009-present) and the AMS Committee on Planned and Inadvertent Weather Modification (October 2009-present). He is among one of three faculty and one of four members listed by ISI HighlyCited in Geosciences at Colorado State University and the University of Colorado at Boulder, respectively.
Dr. Pielke has published over 370 papers in peer-reviewed journals, 55 chapters in books, co-edited 9 books, and made over 700 presentations during his career to date. A listing of papers can be viewed at the project website: http://cires.colorado.edu/science/groups/pielke/pubs/. He also launched a science weblog in 2005 to discuss weather and climate issues. This weblog was named one of the 50 most popular Science blogs by Nature Magazine on July 5, 2006 and is located at http://pielkeclimatesci.wordpress.com/.
Castro, C.L., R.A. Pielke Sr., and G. Leoncini, 2005: Dynamical downscaling: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS). J. Geophys. Res. - Atmospheres, 110, No. D5, D05108, doi:10.1029/2004JD004721. http://pielkeclimatesci.wordpress.com/files/2009/10/r-276.pdf
Castro, C.L., R.A. Pielke Sr., J. Adegoke, S.D. Schubert, and P.J. Pegion, 2007: Investigation of the summer climate of the contiguous U.S. and Mexico using the Regional Atmospheric Modeling System (RAMS). Part II: Model climate variability. J. Climate, 20, 3866-3887. http://pielkeclimatesci.wordpress.com/files/2009/10/r-307.pdf
Mearns, Linda O. , Ray Arritt, Sébastien Biner, Melissa S. Bukovsky, Seth McGinnis, Stephan Sain, Daniel Caya, James Correia, Jr., Dave Flory, William Gutowski, Eugene S. Takle, Richard Jones, Ruby Leung, Wilfran Moufouma-Okia, Larry McDaniel, Ana M. B. Nunes, Yun Qian, John Roads, Lisa Sloan, Mark Snyder, 2012: The North American Regional Climate Change Assessment Program: Overview of Phase I Results. Bull. Amer.Met Soc. September issue. pp 1337-1362.
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Pielke, R.A. Jr., 2010: The Climate Fix: What Scientists and Politicians Won't Tell You About Global Warming. Basic Books. http://www.amazon.com/Climate-Fix-Scientists-Politicians-Warming/dp/B005CDTWBS#_
Pielke Sr., R.A., 2002: Overlooked issues in the U.S. National Climate and IPCC assessments. Climatic Change, 52, 1-11. http://pielkeclimatesci.wordpress.com/files/2009/10/r-225.pdf
Pielke Sr., R.A.,, 2004: A broader perspective on climate change is needed. Global Change Newsletter, No. 59, IGBP Secretariat, Stockholm, Sweden, 16–19. http://pielkeclimatesci.files.wordpress.com/2009/09/nr-139.pdf
Pielke Sr., R., K. Beven, G. Brasseur, J. Calvert, M. Chahine, R. Dickerson, D. Entekhabi, E. Foufoula-Georgiou, H. Gupta, V. Gupta, W. Krajewski, E. Philip Krider, W. K.M. Lau, J. McDonnell, W. Rossow, J. Schaake, J. Smith, S. Sorooshian, and E. Wood, 2009: Climate change: The need to consider human forcings besides greenhouse gases. Eos, Vol. 90, No. 45, 10 November 2009, 413. Copyright (2009) American Geophysical Union. http://pielkeclimatesci.wordpress.com/files/2009/12/r-354.pdf
Pielke Sr., R.A., 2010: Comments on “A Unified Modeling Approach to Climate System Prediction”. Bull. Amer. Meteor. Soc., 91, 1699–1701, DOI:10.1175/2010BAMS2975.1, http://pielkeclimatesci.files.wordpress.com/2011/03/r-360.pdf
Pielke Sr, R.A., 2013a: Mesoscale meteorological modeling. 3rd Edition, Academic Press, in press.
Pielke Sr., R.A. 2013b: Comment on “The North American Regional Climate Change Assessment Program: Overview of Phase I Results.” Bull. Amer. Meteor. Soc., in press. doi: 10.1175/BAMS-D-12-00205.1. http://pielkeclimatesci.files.wordpress.com/2013/02/r-372.pdf
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