Download The impact of climate change on seasonal snow

The impact of climate change on seasonal snow conditions in New Zealand
Prepared by: Jordy Hendrikx
Prepared for: FRST Tourism and CC
Version: 2 (March 10, 2010)
1.
Overview:
Seasonal snow directly affects New Zealand's economy through the energy,
agriculture and tourism sectors. Depending on the pace of global climate change over
the coming decades, the extent, depth, duration and quality of snow in New Zealand is
likely to be subject to significant change. Regional climate models currently suggest
that the maximum daily temperatures in the Southern Alps could rise by more than
4°C by the end of this century (Dean et al., 2006). It has also been suggested that
there will be increased extreme precipitation events and more precipitation in the west
(Ministry for the Environment, 2008). The impact of this warming and change in
precipitation on individual ski fields, catchments and regions is unknown and needs to
be investigated. Research in this area will inform decisions on long term infrastructure
planning and development, snow and water resource availability and change and the
likely impacts on alpine tourism and biodiversity. These impacts are both direct,
through changes in snow cover, and also indirect as impacts on; for example, melt
water impacts on hydro-lake inflows, tourism numbers and water availability for
agriculture.
To examine the potential changes on seasonal snow and their direct and indirect
impacts, a two tiered approach has been adopted. The first tier, or project will focus
on snow modelling and adaptation in New Zealand. This research indicates that at
nearly all elevations, and across all models, the A1B “mid-range” emissions scenario
for the 2040s and 2090s scenarios result in a decrease in snow in New Zealand as
described by all of our summary statistics: Maximum snow; Snow duration;
Percentage precipitation that is snow. The second tier, as a case study within a tourism
project will use the information from the previous work and build on it, to allow for
direct comparison between New Zealand and Australian snow models and permit
direct assessment of relative impacts on these ski fields. We hypothesis that the
relative change in seasonal snow at these trans Tasman locations are likely to be at
least as important, if not more important, than the absolute change at each location
with respect to driving winter tourism.
2.
Aims:
The aim of the first project is to improve understanding of the impacts of past and
future climate change on natural snow cover in New Zealand, and to help assess the
implications of such changes for New Zealand at a broad national scale, but also at an
individual Ski field scale. Subsequent work will assess a ski field’s ability to adapt to
a new climate, primarily through its opportunity for snowmaking. This work is
currently funded by Foundation for Research Science and Technology (FRST contract
C01X0804) and the Ski Areas Association of New Zealand (SAANZ).
The aim of the second project is to take the above mentioned work and use it to
permit direct comparison with the Australian Industry. This work will be a case study
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nested within a wider three year tourism project titled “Preparing the Tourism Sector
for Climate Change” (FRST contract LINX0903). The larger project aims to examine
tourism’s vulnerability and adaptation options and will take a national overview and
use case studies. The ski industry case study will be the most detailed of the three case
studies in this project. Previous work by Hennessey et al., (2003) has modeled
changes in seasonal snow for Australia, but due to different model parameterizations,
and emissions scenarios, direct comparison to the result of the New Zealand work is
difficult and likely to be misleading. This project will use the same model for both
New Zealand and Australia, for the same emissions scenario, to permit direct
comparison of climate change impacts. We hypothesis that the relative change in
seasonal snow at these trans Tasman locations are likely to be at least as important, if
not more important, than the absolute change at each location with respect to driving
winter tourism.
3.
Approaches:
The overall aims of the first project are to make the first quantitative estimates of the
potential effects of climate change on seasonal snow in New Zealand. We will use the
NIWA Snow model (Clark et al., 2009) which has been validated using a range of
metrics, including a robust water balance approach. Three key metrics are used within
the snow model; Maximum snow in a given year; Snow duration (length of time that
snow is on the ground); Percentage precipitation that is snow. This research is nested
within a wider multi-pronged approach which is being undertaken at NIWA to
combine improved monitoring, process understanding and modeling.
The second and more directly tourism related project will use the NIWA Snow model
to model impact of seasonal snow in Australia. We will use the same snow model as
used in New Zealand, with the same model parameters and emissions scenarios. The
main challenges posed to this approach is around data formats, data access and model
validation data. Work is currently underway with both Kevin Hennessey (CSIRO) and
David Jones (BOM) to obtain and standardize input and validation data sets.
4.
Methods:
The current work uses a “middle of the road” (A1B) emissions scenario to examine
the potential impacts of climate change on seasonal snow (Figure 1).
The IPCC uses a range of scenarios, known as the “SRES scenarios”. These are
divided into four families, or storylines, that describe distinctly different future
developments of economic growth, global population and technological change.
These four families are known as ‘A1’, ‘A2’, ‘B1’ and ‘B2’ and the A1 family is
further subdivided into three groups (A1FI, A1T and A1B) (Ministry for the
Environment, 2008). A description of the SRES scenarios are summarized in the box
on the following page.
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Figure 1
IPCC multi-model temperature projections for selected scenarios. The
grey bars to the right show the range in global warming for the
scenarios used in Ministry for the Environment (2008)
The A1 scenario family describes a future world of very rapid economic growth,
global population that peaks mid-century and declines thereafter, and the rapid
introduction of new and more efficient technologies. A major underlying theme is
convergence among regions of the globe, with a substantial reduction over time in
regional differences in per capita income. The A1 family is split into three groups that
describe alternative directions of technological change in the energy system: fossil
intensive (A1FI), non-fossil energy sources (A1T) or a balance across all sources
(A1B).
The B1 scenario family describes a convergent world with the same population
trajectory as in the A1 storyline, but with rapid changes towards a service and
information economy, with reductions in material intensity and the introduction of
clean and resource-efficient technologies.
The A2 scenario family describes a very heterogeneous world, with the underlying
theme of self-reliance and preservation of local identities. Global population increases
continuously, economic development is regionally oriented, and per capita economic
growth and technological change are more fragmented and slower than in the other
storylines.
The B2 scenario family describes a world that emphasises local solutions to economic,
social and environmental sustainability (ie, a heterogeneous world as in A2). Global
population increases continuously at a rate slower than A2, with intermediate levels of
economic development, and less rapid and more diverse technological change than in
the B1 and A1 storylines.
Source: Ministry for the Environment, (2008), p.118.
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The IPCC Fourth Assessment Report has over 20 different GCMs, for this work we
have selected the “best” 12 that simulate the past climate for NZ and South Pacific
and used the average of the downscaling for the A1B emissions scenario for these 12
models. These results can be seen in Figure 2.
2A
Figure 2
2B
(2A) Projected annual mean precipitation change between 1980-1999
and 2030-2049 and (2B) Projected annual mean temperature change
between 1980-1999 and 2030-2049. Based on an average over 12
climate models for an A1B emissions scenario.
The changes in temperature and precipitation are provided for monthly periods. These
values have been disaggregated to daily, then hourly values and then applied to the
historical hourly data for use in the snow model. The snow model uses interpolated
hourly precipitation and temperature data for a twenty year historical period (19801999) to simulate “current snow”. These historic data can be thought of as “climate
surfaces” which are data on an approximately 5km by 5km grid of New Zealand (Tait
et al., 2008). We then applied the change fields described above to the historical
climate data to simulate climate for the periods 2030-2049 (2040s) and 2080-2099
(2090s). These new, synthetic climate data are then used to simulate future season
snow “2040s snow” and “2090s snow” respectively. Comparisons are then undertaken
between the current snow and the 2040s snow and 2090s snow. A flow diagram of the
methodology is shown in Figure 3. This methodology is widely know as a delta
change methodology, as it uses the historical data for a base model run and then
perturbs this data according to a change field to re-run the model for future scenarios
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Modify historical
data
to simulate
Climate Change
scenarios
(Average A1B)
Historical
Climate
surfaces:
Temperature
Precipitation
Daily maps
of
interpolated
Precip &
Temp data
on a grid of
0.05
degrees.
Simulate
Historical
Snow
(Validated)
COMPARE
2040s
Climate
surfaces:
2090s
Climate
surfaces:
Temperature
Precipitation
Temperature
Precipitation
Simulate
2040s
Snow
Simulate
2090s
Snow
COMPARE
Figure 3
Flow diagram showing the delta change scenario methodology used for
simulating current or historical seasonal snow (1980-1999) and future
seasonal snow for the 2040s (2030-2049) and 2090s (2080-2099).
5.
Results – Project 1
5.1
National scale
National scale results from the snow modeling work are shown in Figure 4. These
maps are for the maximum annual snow water equivalence (SWE) for the South
Island of New Zealand for the current, 2040s and 2090s under an A1B emissions
scenario. SWE rather than snow depth is used in our model as it is a better measure of
total snow volume. The maps in figure 4 are the graphical output from running the
snow model in over 250,000 individual model elements. Maps for change in the snow
duration and fraction of precipitation that is snow are also available, but not presented
here.
Maximum SWE shows a significant decrease at nearly all elevations, except for the
very highest elevation locations (above approx 2900m) for the 2040s scenario, where
a very slight increase is observed. This is likely due to the influence of the increased
precipitation in the alpine regions being of greater impact than the increase of
temperature on seasonal snow at these highest elevations. However, by the 2090s
scenario there is a significant decrease in maximum SWE at all elevations. The
temperature increase now has a greater impact than the increase in precipitation can
accommodate for at all elevations. The largest change (in absolute amounts) in both
the 2040s and 2090s is along the Main Divide and into Fiordland. These are locations
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that currently have the greatest values of SWE. In relative terms, the greatest
percentage change is at low elevations areas (Figure 5). By the 2040s the decrease is
almost 40% at 1000m and by the 2090s the decrease is about 60% at 1000m. At
2000m the decrease in SWE is slightly less marked, with about a 10% decrease by the
2040s and 30% by the 2090s. At elevations below 1000m there is a greater percentage
decrease in average SWE as one approaches sea level, with average maximum SWE
reducing by approximately 50% in the 2040s and 70% in the 2090s near sea level.
Maximum annual
snow accumulation
(mm)
1500+
Figure 4
Maximum snow accumulation (SWE in mm) for current, the 2040s and
2090s for an A1B emissions scenario.
Figure 5
Percentage difference in maximum snow accumulation (average within
100m elevation bands) for the 2040s (green) and 2090s (blue) A1B
scenario, compared to the current. Elevation is along the x axis from 0
to 3700m and relative percentage change is on the y axis from +100%
to -100%.
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The results presented here are the first quantitative assessments of the potential affects
of climate change on seasonal snow in New Zealand. The results are somewhat
expected and are consistent with our understanding of snow processes – i.e. much less
snow at low elevations, and marginally more at high elevations. This result is also
comparable to the results of other research in similar mid-latitude settings (e.g.
Norway). Roald et al., (2002) reported that the model runs for Norway showed the
winters become warmer, with snow storage declining at low altitudes, but increase in
the higher altitudes as a result of greater precipitation. However, the elevation at
which this increase in SWE occurs at is very high in this study at 2900m. The area of
the South Island above 2900m is less than 0.01%, so in terms of total snow storage
and snowmelt processes, this additional snow is negligible, especially when compared
with the losses at lower elevations.
In strong contrast to the slight increase at high elevations, the decrease in all summary
statistics (snow duration, percentage of precipitation that is snow, mean maximum
SWE in each year) at elevations at and below 1000m (i.e. 79% of the South Island by
area), suggests a very marked change to low elevation snow in the South Island of
New Zealand. If we consider average maximum SWE, this work suggests that by the
2090s at 1000m less than ½ of the current day SWE will be present.
In terms of snow as a resource for the ski / snow industry, the decrease of snow at
elevations of their concern (1000 to 2000m) will become increasingly problematic,
depending on their ability to adapt. If we again consider the relative decrease in
average maximum SWE at 1000m and 2000m, typically the lower and upper limits of
ski-fields, we observe that by the 2040s, there is a 40% and 10% reduction in SWE at
1000 and 2000m respectively. By the 2090s this is even more, with a decrease of
about 60% and 30% respectively.
Assuming that there are enough periods of cold, sub-zero weather, then this reduction
in SWE may still be within the adaption zone, as ski-fields can make additional snow
(provide there is sufficient water). Future work aims to look in more detail at each
individual ski field and assess their ability to mitigate or adapt to the effects of climate
change, mainly through snow making.
5.2
Site scale
National scale modeling provides an overview of the change in state of the snow
resource. However, these model outputs are too coarse to provide any valuable insight
into local scale or site scale changes. The national model was calibrated and then
validated using mainly streamflow. At a local scale, we will re-calibrated the snow
model for each site specifically. This accounts for processes that are not explicitly
accounted for in the snow model, such as wind drifting and re-deposition. The
accuracy of the snow model is therefore tied to the availability and quality of local
observations. Snow depth and density observations have been obtained from the
SAANZ members and range in their quality and length. Future work will use these
data for site specific assessments of the potential change in seasonal snow.
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Discussions are currently also underway with the importers of two snowmaking
systems to obtain the snowmaking production rates at given temperatures and
humidity. Once this data has been obtained, quantification of the total available
“snowmaking window” can be calculated and potential snow volumes calculated for a
future climate for each site. This will permit assessment of individual site adaptation
opportunities.
6.
Progress – Project 2
6.1
Overview of previous work in Australia
In 2003 the Australian Ski Areas Association in collaboration with several Australian
government agencies commissioned CSIRO to undertake a study to assess the impact
of climate change on seasonal snow in mainland Australia. That study (Hennessey et
al., 2003) has proven very useful for the Australian ski, hydro power, tourism and
land management industry.
Hennessey et al., (2003) used two scenarios in the model, both of which were equally
likely, but associated with uncertainties. The low impact scenario used the lowest
projected warming combined with the highest estimate of increased precipitation. The
high impact scenario used the highest projected warming with the highest estimate of
decreased precipitation. Under the low impact and the high impact scenarios
respectively, the total alpine area with an average of at least one day of snow cover
decreases 10-39% by 2020, and 22-85% by 2050. The area with at least 60 days of
cover shrinks 18-60% by 2020, and 38-96% by 2050.
Figure 8
Simulated 20 year average snow depth profiles at Mt Hotham (1882m)
for the present (1979-1998), 2020 and 2050. (Hennessey et al., 2003).
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The high impact scenario for 2050 leads to very large reductions in season duration
and peak depth at all sites. Season durations are typically reduced by around 100 days,
which leaves only the highest sites with durations of more than ten days. Maximum
depths shrink to less than 10% of their present value and occur much earlier in the
season (Hennessey et al., 2003).
The average number of hours suitable for snow-making declines by 2-7% for the low
impact scenario and by 17-54% for the high impact scenario. The potential snowmaking volume is reduced by 3-10% under the low impact scenario, and by 18-55%
under the high impact scenario. Essentially, under their scenarios, there will be a
substantial reduction in natural seasonal snow coverage in Australia.
6.2
Key limitations & differences
The CSIRO snow model has parameterised both the accumulation and melt terms
differently and more simply than the NIWA Snow model. CSIRO intend to recode
their model to utilise the developments in the NIWA snow model to better account for
different snowmelt processes.
The CSIRO snow model was driven with monthly temperature and precipitation data
on a 2.5km grid. The NIWA snow model currently operates on hourly data, but can
operate on daily, weekly or monthly. Hourly resolution is generally more preferable
as it will allow for better representation of processes such as storm events.
The CSISO report uses a combined “worst case” and “best case” temperature and
precipitation scenarios which are derived from the IPCC Third Assessment Report. A
standard emissions scenario from the Forth Assessment report or representative
concentration pathway will be more useful for direct comparison of impacts.
6.3
Overview of progress with Australian data and snow modelling
Work is currently underway with both Kevin Hennessey (CSIRO) and David Jones
(BOM) to obtain and standardize precipitation and temperature data sets. Validation
data will also need to be obtained, and this is also currently underway with Snowy
Mountain Hydro and the Ski Areas Association of Australia.
CSIRO are interested in modifying their snow model to better represent snow melt.
Jordy Hendrikx has provided them with a copy of the paper by Clark et al., (2009)
which describes this fully.
7.
Summary
At nearly all elevations, and all models, the A1B 2040s and 2090s scenarios result in
a decrease in snow in New Zealand as described by all of our summary statistics:
Maximum snow; Snow duration; Percentage precipitation that is snow.
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While the results we present here are consistent with our understanding of snow
processes, we must urge caution in their use. This work has only used a single middle
of the road downscaled emissions scenario (A1B), and a single pattern of change (the
12-model average) to generate our climate change scenarios for the 2040s and 2090s.
The change scenario methodology employed is also a very simple approach; in
particular that it does not make any allowance for changes in extremes. These extreme
events are of great importance, particularly for ski-fields as often one (large) well
timed storm can be more beneficial for a ski-field than the same amount of snow
arriving in small amounts over the space of a few weeks. Future work will further
investigate these and other related issues through the coupling of a regional climate
model with the snow model, to simulate future climates and impacts
8.
Outlook and next steps
Project 1:
Snowmaking data needed from New Zealand industry.
Snow volumes / areas required needed from SAANZ members.
Calculation of future snow making window needed
Synthesis report to SAANZ
Project 2:
Australian data needed to run snow model
Validation data needed to check and assess snow model
Change fields for precipitation and temperatures needed for a range of emissions
scenarios for a range of time intervals (i.e. 2020, 2040, 2090).
9.
Recent relevant presentations
SIRG and Public Lecture:
Fitzharris, B.B., and Hendrikx, J., 2010. Climate Change, Snow, Ice and Tourism.
Snow and Ice Research Group New Zealand, Invited public lecture at the
Copthorne Hotel, Queenstown, New Zealand, 16 February 2010.
Hendrikx, J., Clark M., Hreinsson E, Kerr, T, Poyck, S., Harper, A, Tait A, Woods R.,
and Mullan, B., 2010. Current & future seasonal snow in New Zealand: An
update on the monitoring and modelling. Snow and Ice Research Group New
Zealand, Lakeland Park, Queenstown, New Zealand, 15-17 February 2010.
New Zealand Hydrology Conference:
Hendrikx, J., Clark, M., Hreinsson, E., Tait, A., Woods, R., Slater, A., and Mullan, B.
The future of seasonal snow in New Zealand: An analysis using 12 GCMs. New
Zealand Hydrology Conference, Whangarei, 23-27 November, 2009.
Poyck, S., Hendrikx, J., Woods, R.A., Clark, M.P., Hreinsson, E.O., McMillan, H.,
Mullan, A.B., Tait, A. Modelling impacts of climate change on water resources
and floods in a snow-influenced catchment. New Zealand Hydrology
Conference, Whangarei, 23-27 November, 2009.
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SAANZ Conference:
Hendrikx. J., 2009. An update on snow and climate change research. Ski Areas
Association of New Zealand meeting (SAANZ), Coronet Peak, Queenstown, 34 November, 2009.
Glacier & Climate Modelling Workshop:
Hendrikx, J., Clark, M., Hreinsson, E., Poyck, S., Tait, A., Woods, R., Slater, A.,
Mullan, B., McMillan, H., 2009. Regional modelling of future New Zealand
climate: Linked environmental models. Joint Antarctic Research Institute (JARI),
1st Glacier & Climate Modelling Workshop, Victoria University, Wellington, 2
November, 2009.
Poyck, S., Hendrikx, J., Hreinsson, E., Woods, R., Clark, M., Tait, A., 2009.
Combined snow and streamflow modelling to estimate impact of climate change
on water resources and floods. Joint Antarctic Research Institute (JARI), 1st
Glacier & Climate Modelling Workshop, Victoria University, Wellington, 2
November, 2009.
10.
References
Clark, M., Örn Hreinsson, E., Martinez, G., Tait, A., Slater, A., Hendrikx, J., Owens,
I., Gupta, H., Schmidt, J. & Woods, R., 2009. Simulations of seasonal snow for
the South Island, New Zealand. Journal of Hydrology (NZ), 48(2), 41-58.
Dean, S., Mullan, B.A., Renwick, J., 2006. More shorts and gumboots? New Zealand
climate at the end of this century as simulated by a regional climate model.
Paper presented at the Resource Management under Stormy Skies: Water
Allocation @ the Crossroads? Joint conference of the New Zealand
Hydrological Society, the New Zealand Association of Resource Management,
and the Meteorological Society of New Zealand, Christchurch, November 2006.
Tait, A., Henderson, R.D., Turner, R., Zheng, X., (2006). Thin plate smoothing spline
interpolation of daily rainfall for New Zealand using a climatological rainfall
surface. International Journal of Climatology 26(14), 2097-2115.
Hendrikx J., Clark, M.P., Slater, A., Woods, R.A., Orn Hreinsson, E., Tait, A., Mullan,
B., 2009. Simulations of seasonal snow in New Zealand: Past and Future. 9th
International Conference on Southern Hemisphere Meteorology and
Oceanography (ICSHMO), Melbourne, Australia, 9-13 February, 2009.
Hennessy, K., Whetton, P., Smith, I., Bathols, J., Hutchinson, M., Sharples, J., 2003.
The impact of climate change on snow conditions in mainland Australia. CSIRO
Atmospheric Research, Aspendale, Victoria, Australia, 47 pp.
Ministry for the Environment (2008). Climate Change Effects and Impacts
Assessment. A Guidance Manual for Local Government in New Zealand. 2nd
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Edition. Prepared by Mullan, B; Wratt, D; Dean, S; Hollis, M. (NIWA); Allan,
S; Williams, T. (MWH NZ Ltd), and Kenny, G. (Earthwise Consulting Ltd), in
consultation with Ministry for the Environment. NIWA Client Report No.
WLG2007/62. 156 p.
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