![Go to home page [Ministry for the Environment]](/images/logo.gif){kind=logo}
Appendix 3: Further Details of Projected New Zealand Climate Change
A3.1 Introduction
Chapter 2 provides quantitative and qualitative projections of future change in a number of key climate variables for New Zealand. This section goes into additional technical detail for some of the climate variables. The results are drawn from a number of previous studies, but particularly from the IPCC Third Assessment (Chapter 9, Cubasch et al 2001), interpreted within the local context, from the data analysed for the New Zealand downscaling study of Mullan et al (2001a), and from the CLIMPACTS 2000 National Assessment discussed in Mullan et al (2001b). Other information on New Zealand impacts of global warming can be found in the syntheses of Pittock and Wratt (2001) and Ministry for the Environment (2001).
Developing robust quantitative scenarios of change for New Zealand, consistent with the full IPCC emissions range, requires scaling the AOGCM changes that apply to a particular scenario of 1% per annum compounding CO2 concentration. This robust scaling (see Appendix 2) was applied to monthly mean AOGCM data for precipitation, mean temperature and sea-level pressure. Other climate variables, such as those affecting daily temperature and precipitation extremes, have not been analysed in sufficient detail across a range of AOGCMs to allow such scaling to be applied. Projections for these latter variables thus tend to be more qualitative, or based on the global climate model simulations for a mid-range emissions scenario.
A3.2 Projections of key climate variables
The full IPCC range of projections is described below and in Chapter 2, where quantitative evaluation is feasible. The extremes are denoted by 'lowest' and 'highest' in the text and accompanying figures and tables. In addition, a 'mid-IPCC' projection is also produced, that effectively represents a mid-range IPCC emissions scenario where the regional response is averaged over all the climate models available.
A3.2.1 Mean temperature
The robust scaling to force the AOGCM global changes to match the IPCC extremes (Table A2.1 in Appendix 2) was applied to the 58 temperature sites (Figure A3.1) used in the downscaling of Mullan et al (2001a). The results are shown in Figure 2.2 for lowest, mid-IPCC and highest annual mean changes in the 2030s and 2080s, and in Figures 2.3 and 2.4 for the mid-IPCC seasonal mean changes. The lowest and highest projections for each season are given in Figures A3.2 to A3.5 of this Appendix. Tables 2.2 and 2.3 tabulate the same results for each Regional Council district. In general, the 'lowest' scenario derives from a combination of the lowest IPCC emissions scenario and the model with the smallest (positive) local temperature response, whereas the 'highest' scenario derives from a combination of the highest IPCC emissions scenario and the model with the largest local temperature response.
It is clear from the figures that the extreme temperature changes do not show much spatial variation, although there is a weak tendency for greater warming in the east and north of the country. For the mid-IPCC scenario, annual warming by the 2030s ranges from +0.5°C above 1990 levels in Fiordland to +0.7°C around East Cape, Coromandel and Northland. By the 2080s, this warming has increased to +1.5°C in Fiordland to +2.0°C in the northeast of the North Island. Seasonally, the strongest warming occurs in winter. Indeed, in the summer season the 2030s low extreme shows almost no warming or even slight cooling relative to 1990. This could be due to internal ('natural') variability in the model, or to an increased southerly wind component (section 2.2.7), or to increased westerlies in the model (whereby stronger westerlies drive the land temperature closer to the upstream sea temperature which is lower than the land temperature in summer).
The temperature changes of Tables 2.2 and 2.3 are based on a total of 58 sites nationally, grouped into Regional Council regions containing anywhere from a single site (Taranaki, Gisborne and Nelson) to 10 sites (Canterbury). Averaging over all temperature sites gives us a New Zealand-average value. In the annual mean, the New Zealand-average warming ranges from 0.16 to 1.30°C by the 2030s, and from 0.47 to 3.53°C by the 2080s. In all cases, these local changes are smaller than the corresponding global warming (Table A2.1).
A3.2.2 Rainfall patterns
The robust scaling of AOGCM global changes was applied to the 92 rainfall sites (Figure A3.1) used in the downscaling of Mullan et al (2001a). The results are shown in Figure 2.5 for the annual mean precipitation changes (lowest, mid-IPCC and highest) in the 2030s and 2080s. Figures 2.6 and 2.7 show the mid-IPCC seasonal changes. The seasonal lowest and highest projections are given in Figures A3.6 to A3.9 of this Appendix. The largest rainfall changes (that is, the largest decreases in the 'lowest' scenario and the largest increases in the 'highest' scenario) derive from a combination of the highest IPCC emissions scenario and the models with the largest (negative or positive) local rainfall responses.
Tables 2.4 and 2.5 give the 'lowest to highest' extreme range for selected sites within each regional council region. The ranges indicated are the values directly from the model changes, after downscaling to the local site and rescaling for the IPCC scenarios, without any attempt to 'round' the results. Undue weight should not be given to the apparent precision of the values.
A3.2.3 Daily temperature extremes
In addition to changes in mean temperature (section 2.2.1), daily temperature extremes (overnight minimum and daytime maximum) will also vary with regional warming. In principle, the extremes of a distribution can be affected both by a change in mean and a change in standard deviation. However, simulations from global climate models suggest potentially large changes in mean (towards warmer conditions), but are generally unclear, or show only small changes, with respect to the standard deviation. Therefore, the simplest assumption is to examine how extremes vary with a shift in the mean only.
Small changes in the mean can have a large effect on the frequency of extreme temperatures, as is illustrated in Box 2.1 of Chapter 2. That simple example illustrates the misunderstanding in the oft-heard statement that 'the climate will become more extreme' under global warming. Obviously, extremes can become either more or less frequent, depending on just what extremes one is considering.
Figure 2.8 shows a specific example for New Zealand temperature extremes of increasing the mean temperature. The maps are reproduced from Mullan et al (2001b), who used the CLIMPACTS framework to link a number of global emission scenarios to various downscaled GCM patterns of New Zealand change. At individual sites, a weather generator (that simulates daily sequences of maximum and minimum temperature and rainfall (Thompson and Mullan 2001)) is run for ranges of future climates. Figure 2.8 shows projected changes in low temperature extremes (days per year with minimum below freezing) and in high temperature extremes (days per year with maximum above 25°C), for one particular AOGCM and two fairly extreme emissions scenarios B1 and A2 (Figure A2.1).
Figure 2.8 indicates large decreases in the number of frost days in the lower North Island and the South Island: anywhere from 5 to 10 fewer days under the B1 scenario to 20 to 30 fewer days under A2, for this particular climate model. (Note that the far north of New Zealand now no longer receives frosts, and so there can be no further decrease). There is a general tendency for a bigger reduction in frost days at the colder (South Island) sites, even though the projected mean warming is less here. Substantial increases in warm days are also indicated, being larger at more northern warmer sites. The changes in these extremes are sensitive to the climate model as well as the emissions scenario. However, the range of temperature extreme changes could be generated for any specific site where daily temperature data are available.
A3.2.4 Extreme precipitation
The IPCC Third Assessment, relying on both observational and modelling studies, declared that more intense precipitation events are 'very likely, over many areas' (Table 1 in Summary for Policymakers, IPCC 2001). This does not necessarily apply to a region as small as that of New Zealand, particularly to those parts where the annual mean rainfall is projected to decrease. However, a warmer atmosphere can hold more moisture (about 8% more for every 1°C increase in air temperature), and so the potential for heavier extreme rainfalls is present.
An alternative way of viewing these systematic increases in average rainfall intensity is to say that a reduction in the return period of heavy rainfall events is expected. The only published estimate of projected changes in return periods for New Zealand is that provided by Whetton et al (1996), who suggested that: by 2030, there would be "no change through to a halving of the return period of heavy rainfall events", and by 2070, "no change through to a fourfold reduction of the return period". This statement was based on analysing daily precipitation time series from a regional climate model, driven by the CSIRO equilibrium global climate model.
More recent results on how daily precipitation extremes could change for New Zealand are available from only one coupled global climate model (Semenov and Bengtsson 2002). These authors present global maps of changes in total rainfall and in the 95-percentile daily value. They also analyse changes in the rainfall distribution. The 'normal' distribution (figure in Box 2.1), which is symmetrical about the mean, is not appropriate for rainfall, which is commonly represented by the so-called 'gamma' distribution (Figure A3.11). The parameters of the gamma distribution are known as the shape factor ('alpha', α) and the scale factor ('beta', β). For alpha less than 1, as is always the situation for New Zealand, the higher the rainfall amount the less frequently it occurs. If alpha increases above 1, then there is a peak mode or most likely rainfall amount. As alpha increases further, the gamma distribution tends to the same shape as the normal distribution. The mean rainfall averaged over days when it rains (the so-called rainfall 'intensity') is simply the product of these two factors (i.e. αβ). The gamma distribution only applies to raindays. The other relevant factor is the likelihood of it raining at all (probability of a wet day, Pw). The change in mean rainfall (section 2.2.2) is therefore the change in the triple product Pwαβ.
Semenov and Bengtsson (2002) map the changes in alpha and beta, as simulated by their model (which corresponds to the MPI model used by Mullan et al 2001a). Outside the tropical and subtropical oceans, alpha generally decreases (by up to 10%) whereas beta generally increases (by up to 40%). Figures A3.10 and A3.11 are an example of how this projection might be applied at a particular site. Thirty years of observed daily winter rainfall at Auckland (Whenuapai) are used to compute the distributional parameters alpha (= 0.735) and beta (= 10.19). By systematically varying alpha and beta from their observed values, we can generate a surface that represents how the daily winter rainfall might change by 2100. In this case, Figure A3.10 shows how the 95-percentile winter daily rainfall amount for the present climate (= 25.0 mm) could change. Over the (shaded) range of alpha/beta parameters suggested by Semenov and Bengtsson (2002), Figure A3.10 shows that this winter extreme rainfall could vary from about 6% less than present (10% decrease in alpha, with no change in beta) to 40% more than present (no change in alpha, with 40% increase in beta). Either of these changes could be made consistent with the scenario of mean rainfall change by adjusting the number of wet days.
Figure A3.11 is a plot of the gamma distribution for this 2100 extreme case of no change in α but a 40% increase in β. The figure shows that a daily winter rainfall amount of about 100 mm, which was only exceeded once in the 30 years of observed record, would become at least 10 times more likely under this particular scenario. Figure A3.12 shows the extreme rainfall data translated into return periods. The data were generated by random sampling from the gamma distribution, firstly using the α,β parameters fitted to winter observations at Auckland, and repeated with a 40% increase in β. Return periods were estimated by fitting an EV1 (Extreme Value type 1) distribution to the highest daily rainfall for each of 30 winters. The result (Figure A3.12) indicates that a rainfall amount with a return period of 50 years under the present climate has a return period of about seven years at 2100 in this worst case. This seven-fold reduction in return period is broadly consistent with the earlier estimates of Whetton et al (1996).
A3.2.5 Snowfall and snowline
It is physically plausible that snow cover will decrease and snowlines rise as the climate warms. However, there are confounding issues. Warmer air holds more moisture, and during winter this moisture could be precipitated as snow at high elevations. Thus, warming does not rule out increased winter snowfall, although the duration of seasonal snow could be shortened.
Surveys of end-of-summer snowline are made each year for the glaciers of the Southern Alps (e.g. Chinn and Salinger 2001). These measurements were begun in the late 1970s, and this length of record is not sufficient to make a good case for how New Zealand snowline varies with temperature. What has been established is that there has been an increase in permanent snow, and a decrease in snowline altitude, with the increased westerlies of the last two decades (see Chapter 3).
Climate change is likely to produce important changes in both electricity supply and electricity demand for New Zealand (Fitzharris and Garr, 1996). Supply will be affected particularly by changes of precipitation into the hydro-catchments of the South Island, whereas national demand will be affected by temperature increases. Snow storage, and the variation of this from year to year, is an important factor in managing the hydro-electric system. Under the present climate, river flows in the South Island tend to be lowest in winter, when the demand for electricity is highest, and rise in spring and summer as snow and glacier melt make significant contributions: currently, about 15% of total runoff is from seasonal snow storage (Fitzharris and Garr, 1996).
A3.2.6 Wind patterns
Monthly mean pressure data are available from the study of Mullan et al (2001a). Changes in pressure gradients have been scaled to the full IPCC range of SRES scenarios, in the same way as mean temperature and rainfall. Results are presented in Table 2.6 for west-east and north-south wind components across central New Zealand. The west-east component is derived from the Auckland to Christchurch pressure difference (positive changes indicate more westerly flow in the mean), and the north-south component from the Hobart to Chatham Island pressure difference (positive changes indicate more southerly flow in the mean).
Table 2.6 shows that under the current climate the mean westerlies across New Zealand have a strength of about 2.9 m/sec in the annual mean, and are substantially stronger in spring than any other season. There is weak southerly flow in the annual mean, and in all seasons individually except summer when it is northerly. The future scenarios show a strong bias towards increasing westerly flow, particularly in the annual mean. By the 2080s, there could be almost no change up to more than double the current mean speed of westerly airflow in the annual mean. Changes in the north-south wind component are less clear cut, although there is a bias towards more southerly in summer and more northerly in winter. These wind changes are partly responsible for the projected weaker warming in summer and greater warming in winter.
A3.2.7 Storms
Mid-latitude storms (also known as extra-tropical cyclones) derive their energy from two sources: the large-scale meridional temperature gradient and the condensation of moisture. Global warming therefore has the potential to alter the intensity or frequency, or both, of extra-tropical cyclones. However, relatively few analyses of model simulations have been done, and there is no general agreement on future change in mid-latitude storms. Carnell and Senior (1998), in a study over the Northern Hemisphere, found a reduced total number of cyclones but a greater number of very intense lows. They attributed these two apparently contradictory changes to a reduced equator-North Pole temperature difference (reducing total numbers), but increased latent heating in a moister atmosphere (more intense convection).
In the Southern Hemisphere, both the energy sources (meridional temperature gradient and latent heating) are predicted to increase under global warming. It would therefore be reasonable to infer that 'more storminess' was likely. Unfortunately, the only analysis of GCM Southern Hemisphere storm track changes that is available (Sinclair and Watterson 1999) was done for the previous generation of equilibrium climate models where the meridional temperature gradient (and westerlies) weakened. These results are therefore not relevant anymore, when the latest transient, or coupled ocean-atmosphere, models indicate an increase in the mean westerly flow associated with an increase in the temperature gradient between equator and high southern latitudes.
While it therefore appears likely that 'storminess' will increase in the Southern Hemisphere this century, we cannot yet say whether this would mean more intense storms, or a higher frequency of passing cold fronts, or some combination of these. Moreover, a general increase in the Southern Hemisphere does not equate with an increase locally in the small sector of the hemisphere that New Zealand occupies. The regional changes would be sensitive to changes in prevailing wind strength and direction, which vary considerably between models.
Figure A3.2: Projected changes in seasonal mean temperature (in °C) relative to 1990 for 2020-2049 (2030s): low end of IPCC range
Figure A3.3: Projected changes in seasonal mean temperature (in °C) relative to 1990 for 2020-2049 (2030s): high end of IPCC range
Figure A3.4: Projected changes in seasonal mean temperature (in °C) relative to 1990 for 2070-2099 (2080s): low end of IPCC range
Figure A3.5: Projected changes in seasonal mean temperature (in °C) relative to 1990 for 2070-2099 (2080s): high end of IPCC range
Figure A3.6: Projected changes in seasonal precipitation (in %) relative to 1990 for 2020-2049 (2030s): low end of IPCC range
Figure A3.7: Projected changes in seasonal precipitation (in %) relative to 1990 for 2020-2049 (2030s): high end of IPCC range
Figure A3.8: Projected changes in seasonal precipitation (in %) relative to 1990 for 2070-2099 (2080s): low end of IPCC range
Figure A3.9: Projected changes in seasonal precipitation (in %) relative to 1990 for 2070-2099 (2080s): high end of IPCC range
Figure A3.10: Percentage change in Auckland 95-percentile daily rainfall amount, as a function of changes in the parameters (alpha and beta) of the gamma distribution
Figure A3.11: Effect on probability of extreme daily winter rainfall at Auckland, for an increase in the beta parameter of the gamma distribution, but no change in the alpha parameter
Figure A3.12: Effect on return period of extreme daily winter rainfall at Auckland, for an increase in the beta parameter of the gamma distribution, but no change in the alpha parameter
References
Carnell RE, Senior CA. 1998. Changes in mid-latitude variability due to increasing greenhouse gases and sulphate aerosols. Clim Dyn 14: 369-83.
Chinn TJ, Salinger MJ. 2001. New Zealand Glacier Snowline Survey, 2000. NIWA Technical Report 98. Wellington.
Cubasch U, Meehl GA, Boer GJ, et al. 2001. Projections of future climate change. In JT Houghton, Y Ding, DJ Griggs, et al (eds) Climate Change 2001: The scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge and New York: Cambridge University Press, 525-82.
Fitzharris BB, Garr C. 1996. Climate, Water Resources and Electricity . In: WJ Bouma, GI Pearman, MR Manning (eds) Greenhouse, Coping with Climate Change. CSIRO Publishing, Collingwood, Australia, 263-80.
IPCC. 2001. Climate Change 2001: The scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. JT Houghton, Y Ding, DJ Griggs, et al (eds). Cambridge and New York: Cambridge University Press.
Ministry for the Environment. 2001. Climate Change Impacts on New Zealand. Report prepared by the Ministry for the Environment as part of the New Zealand Climate Change Programme. Publication ME396. Wellington: Ministry for the Environment.
Mullan AB, Wratt DS, Renwick JA. 2001a. Transient model scenarios of climate changes for New Zealand. Weather and Climate 21: 3-34.
Mullan AB, Salinger MJ, Thompson CS, et al. 2001b. The New Zealand climate: present and future. In: RA Warrick, GJ Kenny, JJ Harman (eds) The Effects of Climate Change and Variation in New Zealand: An Assessment Using the CLIMPACTS System. Chapter 2. IGCI, University of Waikato, 11-31.
Pittock AB, Wratt DS. 2001. Australia and New Zealand. Climate Change 2001: Impacts, adaptation and vulnerability. Chapter 12. Published for the IPCC by Cambridge University Press, 591-639.
Semenov VA, Bengtsson L. 2002. Secular trends in daily precipitation characteristics: greenhouse gas simulation with a coupled AOGCM. Climate Dynamics 19: 123-40.
Sinclair MR, Watterson IG. 1999. Objective assessment of extratropical weather systems in simulated climates. J Climate 12: 3467-85.
Thompson CS, Mullan AB. 2001. Comparing the rainfall producing models in stochastic weather generators. Weather and Climate 21: 35-46.
Whetton P, Mullan AB, Pittock AB. 1996. Climate change scenarios for Australia and New Zealand. In: WJ Bouma, GI Pearman, M Manning (eds) Greenhouse '94: Coping with climate change. Melbourne: CSIRO/DAR, 145-68.
