ASSESSING CLIMATE FUTURES: A CASE STUDY Andrew Wilkins 1, Leon van der Linden 1, 1. SA Water Corporation, Adelaide, SA, Australia ABSTRACT This paper examines two techniques for quantifying GCM derived climate projection uncertainty. The advantages and disadvantages of each approach are discussed. The techniques are then applied to a case study for a single derived climate variable at a weather station in Mount Gambier in the South East of South Australia. Climate Futures finds figures within 5% of those found using an ensemble approach, however some information appears to be lost for extreme values. INTRODUCTION Practitioners in Water Resource Management are increasingly utilising models to make projections for water resources and demands. The climate is a significant driver for the water cycle, and as such climate data is important for models used in Water Resource Management. Data generated by General Circulation Models (GCMs) is commonly used for this purpose. However, a significant amount of processing is needed to use GCM data for modelling; climate researchers commonly use ensemble approaches which consider all the outputs from a large group of GCMs. To reduce the amount of effort that is needed to utilise GCM data for projections, practitioners are increasingly turning to methods like Climate Futures, a technique developed by the CSIRO, to simplify analysis of GCM data. There is little published information on how effectively techniques like Climate Futures represent the uncertainty in climate projections, and what, if any, information the methods discard in simplifying the analysis. Publications by the CSIRO on Climate Futures have focussed on the process and application to case studies, demonstrating the efficiency of the technique, but not the accuracy. This paper starts to address that gap by comparing Climate Futures to an ensemble approach for a single climate derived variable at a weather station in Mount Gambier in the South East of South Australia. BACKGROUND Ensemble Approaches Ensemble approaches have been widely adopted by researchers to help quantify the uncertainty in GCM derived climate projections. The approaches use results from realisations of multiple GCMs as samples of the underlying distribution of potential climates. As the number of samples taken increases, a distribution that is fit to the samples is expected to approach the true underlying distribution that the GCMs are able to represent. Many variations on the ensemble approach exist. For example, some ensemble approaches weight the contributions from each GCM, while others utilise a subset of the available GCMs for analysis. In a Good Practice Guidance Paper prepared for the IPCC Expert Meeting on Assessing and Combining Multi Model Climate Projections, Knutti et al. 2010 note that a multiple model ensemble is rarely a direct measure of uncertainty, but that the spread [of GCMs] help to characterise uncertainty. Knutti et al. also note that Forming and interpreting ensembles for a particular purpose requires an understanding of the variations between model simulations and model set-up and clarity about the assumptions. It is clear that whilst ensemble approaches have been identified as a best practice technique, they require a strong understanding of the underlying GCMs, the use of stochastic analysis and the ability to use specialist software that facilitates the significant data processing needed. This creates a large obstacle for most practitioners. Another major concern for practitioners is that, the use of ensemble approaches removes the internal consistency within the projected data (Clarke et al, 2011). Where these relationships are important, such as in rainfall-runoff modelling, an ensemble approach may not be suitable. Climate Futures Developed by the CSIRO (Clarke et al., 2011, Whetton et al., 2012), Climate Futures is an easily applicable approach, which allows practitioners to identify three GCMs which are assumed to envelope the extent of the uncertainty in climate projections.
First, Representative Climate Futures are defined by creating categories for the level of change in key climate parameters; commonly temperature and rainfall are used. Each GCM is then categorised into a Representative Climate Future based on the level of change over a specified period. The best case, worst case and most likely case Representative Climate Futures can then be identified, and a single GCM selected as an example of the conditions for that Climate Future. Because Climate Futures only uses the change in selected variables between two climatological periods and then allows three specific GCMs to be selected, the volume of data for processing is significantly less than that required for ensemble approaches. Further, the three selected GCMs can be used independently and as such, the internal consistency within the projected data can be maintained. EXPERIMENT Case Study To compare Climate Futures with an ensemble approach, a case study was considered for Weather Station 26061 (Mount Gambier Aero) located near the City of Mount Gambier in the South East of South Australia. A single derived climate variable, Cooling Degree Days 12 (CDD12) was selected as the parameter for the analysis. It should be noted that CDD12 is dependent only on temperature data. Downscaled Climate Projections Whilst GCMs provide realistic projections for the future climate, they generally do so at a very low resolution, often using grid areas of 60-100 km by 60-100 km square, that are not able to represent the intricacies of local weather behaviours effectively. Downscaling techniques apply the trends from the GCM data to much higher resolution datasets, commonly based on local observations. The Goyder Institute project Downscaling and climate change projections for South Australia. produced downscaled climate projections utilising a Non-homogeneous, Hidden Markov chain Model (NHMM). The Goyder Institute selected GCMs that were best able to represent South Australian conditions, identifying fifteen from the CMIP5 simulations, for which the NHMM downscaling was conducted. The NHMM uses weather states to generate parameter values based on the underlying climate variability of the historical dataset or GCM used to develop the NHMM. The projections generated by the Goyder Institute provide synthetic datasets for a range of six climate variables (Max Temp, Min Temp, Rainfall, Radiation, Vapour Pressure and Morton s PET) on a daily time step. At each weather station, the Goyder Institute provides one hundred realisations of a historical scenario (for dates between 1976 and 2005), and two representative concentration pathway emissions scenarios (RCP4.5 and RCP8.5) between 2006 and 2100 for each GCM. Methodology One hundred realisations of downscaled climate data for the fifteen AR5 GCMs, for both historical and RCP8.5 scenarios were obtained from the Goyder Institute for the weather station selected for the case study. The CDD12 was calculated for every day in each replicate. These daily figures were then aggregated for each financial year to provide a total annual CDD12 for each replicate from each GCM. For the 2005-2006 financial year, data from both the historical scenario and the RCP8.5 scenario was combined to create a complete financial year. To conduct a climate futures analysis, the total rainfall and average annual temperature were calculated for each GCM for 1990 and 2050. The change in total rainfall and average temperature between these years was then calculated. Each GCM was then categorised into a Representative Climate Future as set out in Table 1. The resulting Climate Futures categorisations are presented in Table 2 and Figure 2. From the Climate Futures analysis, the IPSL- CM5ALR was selected as the worst case GCM, the IPSL-CM5BLR was selected as the best case GCM and the ACCESS 10 was selected as the most likely case GCM. Stochastic Analysis Ensemble approaches inherently utilise stochastic techniques, as a distribution is fit to each set of samples. To compare the GCMs selected using Climate Futures with results from ensemble approaches, a distribution was generated using the data from the Climate Futures GCMs, using a three point distribution fitting technique. The three points were generated using the 5 th percentile of the best case GCM, the 50 th percentile of the most likely case GCM and the 95 th percentile of the worst case GCM. An appropriate distribution was selected by looking at the properties of the parameter being fitted; cooling degree days is defined to be nonnegative and has no fixed upper bound, so it was assumed that the selected distribution should hold similar properties. As the lognormal distribution holds these properties, it was selected as the distribution for use in the analysis. Two ensemble approaches were used on the full set of GCM data. In the first base approach, no information was provided about the shape or bounds on the distribution. All distributions available within @Risk, the software used for this analysis, were considered. The distribution with the lowest
Akaike s An Information Criterion (AIC) selected as the best fit. was In the second version, a lognormal distribution with a lower bound of zero and no upper bound was specified and the software was allowed to find the distribution parameters which provided the best fit. It is assumed that the base ensemble approach will provide the best available representation of the uncertainty in the projected CDD12 as it has the most information available and is free to meet the distribution shape that best represents that information. Techniques which simplify analysis are then assumed to lose some of the information from this representation. As such, in this case study, the primary interest is to understand how much information is lost when changing from performing analysis with a base ensemble approach to performing analysis using Climate Futures. However, in using Climate Futures we also move from an unconstrained stochastic method, to a method where a specific distribution has been selected. This movement can be visualised as a two dimensional space as shown in Figure 1, with the three scenarios considered in this case study marked along with the corresponding comparisons. Figure 1: Schematic of scenarios considered in the Case Study To understand how much information loss could be attributed to the selection of the distribution, a comparison was conducted between the base ensemble approach and an ensemble approach with a lognormal distribution. Comparison was made between this lognormal ensemble approach and the Climate Futures approach, to quantify the loss of information from the full ensemble of GCMs to the three GCMs used in Climate Futures., A final comparison between Climate Futures and the base ensemble was used to check that the breakdown of these steps was reasonable. Comparison of the loss of information between any two approaches was conducted visually (Figure 3 and Figure 4), and numerically by calculating the absolute average percentage difference for the 5 th, 50 th and 95 th percentiles as well as the mean, mode and standard deviation of each distribution over a 30 year window centred on the Financial Years starting on the 1 st July in 1990, 2020, 2030, 2050, 2070 and 2085 (Table 3, Table 4 and Table 5). DISCUSSION AND RESULT ANALYSIS Both Figure 3 and Figure 4 show a step change between 2005-06 and 2007-08. This artefact can be directly attributed to the change from the downscaling based on historical data to downscaling based on RCP8.5 GCM data. Figure 3 shows the two ensemble approaches have relatively stable confidence bands that closely overlap up to 2050. This behaviour is reflected in the figures in Table 3, with the average absolute difference in all statistics being less than or equal to 1% for the 30 year windows around 1990, 2020 and 2050; before increasing to 4% at 2085. This is interpreted as the selection of distribution having a negligible impact prior to 2050, with a slight increase beyond that point, suggesting a possible change in distribution shape for the base ensemble approach in the outer years. In Figure 4, comparing the Climate Futures approach to the lognormal ensemble approach, the 90% confidence bands for the Climate Futures approach appear far more variable, although the variability is lower prior to 2005. This is attributed to the NHMM using trends derived from observations for non-precipitation variables for all GCMs during that period, resulting in less information loss in the period prior to 2005. After 2005 however, the size of the Climate Futures 90% confidence interval increases, growing to a maximum at 2050, before contracting to be smaller than the lognormal ensemble 90% confidence interval by 2085. In both Table 4 and Table 5 the increased size of the Climate Futures 90% confidence interval from Figure 4 is reflected in the changes in the differences in the standard deviations, with the effect being greatest for the 30 year window around 2050, the point at which Climate Futures was calibrated. Looking at the central statistics in both Table 4 and Table 5, there are differences of only up to 4.4% across all time periods, implying that the loss of information is limited to the tails of the distributions. CONCLUSION In this case study, it has been observed that the Climate Futures approach produces distributions with an absolute 30 year average for 5 th, 50 th and 95 th Percentile, mean and mode within 5% of each of the base ensemble approach used. However, it also appears that the Climate Futures process amplifies the significance of the two
extreme GCMs, at the same time losing information about the distribution tails, creating a less stable trend in the 5 th and 95 th percentiles and significantly increasing the standard deviation of the distributions. Bearing in mind the limitations of a single case study, it is suggested that Climate Futures could provide a good estimate for central statistics. Further, the extreme values generated using Climate Futures for periods around the calibration point are likely to be overstated, however there is less confidence in these values outside this period. Finally, due to the high volatility of the distribution tails, it is suggested that extreme values for a single year should not be used in isolation. Opportunities for Further Investigation From this work, a number of questions have arisen which the authors have not had an opportunity to explore: Are these results specific to this case study? How sensitive are the results to the selection of the Climate Futures Analysis period? How sensitive are Climate Futures results to the selection of the GCM for the most likely case? How much information is lost if only a single, most likely case GCM is utilised? Repetition of this work across a range of sites and climate variables, and investigations into these questions would increase the confidence for practitioners in applying GCM derived projections and in selecting a simplified method, such as Climate Futures, as an analysis technique. ACKNOWLEDGEMENT The authors thank Alexander Osti from the Department of Environment, Water and Natural Resources, South Australia for his assistance with this research. REFERENCES Alcoe, D., Gibbs, M., Green, G,. 2012. Impacts of Climate Change on Water Resources Phase 3 Volume 3: Alinytjara Wilurara Natural Resources Management Region, DFW Technical Report 2012/05. Government of South Australia, through Department for Water, Adelaide, South Australia, Australia. Clarke, J.M., Whetton, P.H., Hennessy, K.J. 2011. Providing application-specific climate projections datasets: CSIRO s Climate Futures Framework. Climate Adaptation Flagship, CSIRO Marine and Atmospheric Research, Aspendale, Victoria, Australia Gibbs, M., Green, G., Wood, C,. 2011. Impacts of Climate Change on Water Resources Phase 2: Selection of Future Climate Projections and Downscaling Methodology, DFW Technical Report 2011/02. Government of South Australia, through Department for Water, Adelaide, South Australia, Australia. Knutti, R., Abramowitz, G., Collins, M., Eyring, V., Gleckler, P.J., Hewitson, B., Mearns, L. 2010. Good Practice Guidance Paper on Assessing and Combining Multi Model Climate Projections. In: Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Assessing and Combining Multi Model Climate Projections [Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Midgley, P.M., (eds.)]. IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland. Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., Meehl, G.A., 2010. Challenges in Combining Projections from Multiple Climate Models. Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland. Osti, A., Green, G., 2014. Impacts of Climate Change on Water Resources. Phase 3: Volume 6. Kangaroo Island Natural Resources Management Region, DEWNR Draft Technical report. Government of South Australia, through Department of Environment, Water and Natural Resources, Adelaide, South Australia, Australia. van der Linden, P., Mitchell, J.F.B. (eds.) 2009. ENSEMBLES: Climate Change and its Impacts: Summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, Exeter, UK. Westra, S., Thyer, M., Leonard, M., Lambert, M., 2014, Impacts of Climate Change on Surface Water in the Onkaparinga Catchment Volume 2: Hydrological Evaluation of the CMIP3 and CMIP5 GCMs and the Non-homogenous Hidden Markov Model (NHMM). Draft report prepared for the Goyder Institute, Adelaide, South Australia, Australia. Whetton, P., Hennessy, K., Clarke, J., McInnes, K., Kent, D. 2012. Use of Representative Climate Futures in impact and adaptation assessment. CSIRO Marine and Atmospheric Research, Aspendale, Victoria, Australia
Table 1: Representative Climate Futures Parameter Categories Temperature Category Change in Temperature ( C) Rainfall Category Change in Rainfall (%) Little Change -0.5 to 0.5 Little Change -5 to 5 Slightly Warmer 0.5 to 1.0 Slightly Drier -5 to -15 Warmer 1.0 to 2.0 Drier -15 to -25 Hotter 2.0 and above Much Drier -25 and below Figure 2: GCM change in average projection for Climate Futures Analysis (1990 to 2050) Table 2: Climate Futures matrix for Climate Futures Analysis (1990 to 2050) Rainfall Slightly Warmer (0.5-1⁰C) Warmer (1 to 2⁰C) Hotter (2 to 3⁰C) Much Drier (> -25%) Drier (-15 to -25%) 1 1 Slightly Drier (-5 to -15%) 10 1 Little Change (-5 to 5%) 2
Table 3: Comparison of parameter average absolute percentage difference over a 30 year window between the base ensemble and lognormal ensemble approaches 1990 2020 2050 2070 2085 5 th Percentile 0.6% 0.5% 0.5% 1.2% 1.5% 50 th Percentile 0.3% 0.2% 0.3% 0.6% 0.8% 95 th Percentile 0.5% 0.3% 0.5% 1.4% 1.8% Mean 0.0% 0.0% 0.0% 0.1% 0.2% Mode 1.0% 0.7% 1.0% 2.8% 4.0% Standard Deviation 0.6% 0.4% 0.6% 2.8% 3.8% Table 4: Comparison of parameter average absolute percentage difference over a 30 year window between the Climate Futures approach and the lognormal ensemble approach 1990 2020 2050 2070 2085 5 th Percentile 1.5% 4.3% 4.4% 2.3% 2.5% 50 th Percentile 0.6% 0.9% 0.6% 0.6% 0.6% 95 th Percentile 1.1% 2.8% 4.4% 2.6% 2.5% Mean 0.6% 0.8% 0.7% 0.6% 0.7% Mode 0.6% 1.2% 0.7% 0.5% 0.7% Standard Deviation 9.1% 17.8% 20.8% 11.5% 12.8% Table 5: Comparison of parameter average absolute percentage difference over a 30 year window between the Climate Futures approach and the base ensemble approach 1990 2020 2050 2070 2085 5 th Percentile 1.8% 3.8% 4.8% 3.3% 2.6% 50 th Percentile 0.7% 1.1% 0.7% 1.0% 1.1% 95 th Percentile 1.0% 3.1% 4.0% 2.3% 3.1% Mean 0.6% 0.8% 0.7% 0.6% 0.7% Mode 1.1% 1.8% 1.0% 2.8% 4.4% Standard Deviation 8.8% 18.0% 20.4% 10.8% 14.3%
Figure 3: Comparison of trends in CDD12 distribution statistics for the base ensemble approach and the lognormal ensemble approach for Financial Years between 1975-1976 and 2099-2100
Figure 4: Comparison of trends in CDD12 distribution statistics for the lognormal ensemble approach and the Climate Futures approach for Financial Years between 1975-1976 and 2099-2100