558
A framework for climate hazard assessment geared towards adaptation planning and 559
decision support is described. This single GCM gridbox, delta method-based approach, designed 560
for cities and regions smaller than typical GCM gridbox sizes that face resource and time 561
constraints, achieves comparable results in the New York Metropolitan Region to other 562
statistically and dynamically downscaled products. When applied to high frequency historical 563
data, long-term mean monthly climate changes (which GCMs are expected to simulate more 564
realistically for point locations than other features such as actual long-term mean climate or high 565
frequency statistics) yield dramatic changes in the frequency of stakeholder-relevant climate 566
hazards such as coastal flooding and heat events. While the precise projections should not be 567
emphasized given the uncertainties, they are of sufficient magnitude relative to the historical 568
hazard profile to justify development and initial prioritization of adaptation strategies. This 569
process is now well underway in the New York Metropolitan Region.
570
When climate model results for the New York Metropolitan Region are used only for the 571
calculation of monthly climate change factors based on the differences and ratios between 30-572
17Preliminary analysis reveals that over the New York Metropolitan Region gridbox a slight majority of the GCMs show increasing interannual variance of monthly T and P, while a large majority of the BCSD and NARCCAP RCM projections do.
year future timeslices and a 30-year baseline period, three generalized findings follow. First, 573
using multiple ensemble members from the same GCM provides little additional information, 574
since the 30-year average intramodel ranges are smaller than the comparable inter-model range.
575
Second, the spatial pattern of climate change factors in many regions (including New York City) 576
is sufficiently homogeneous --- relative to the intermodel range --- to justify use of climate 577
change factors from a single overlying GCM gridbox. Finally, for these metrics, newer 578
statistically (BCSD) and dynamically (four NARCCAP RCMs) downscaled products provide 579
comparable results to the GCM single gridbox output used by the NPCC.
580
The checklist in Table 2 provides a series of questions to help inform the selection of the 581
most appropriate climate hazard assessment and projection methods. For example, the delta 582
method is more justified when: 1) robust, long-term historical statistics are available, and 2) 583
evidence of how modes of interannual and interdecadal variability and their local teleconnections 584
will change with climate change is inconclusive. Both these criteria are met in the New York 585
City Metropolitan Region. In contrast, more complex applications (than the delta method) of 586
statistically and dynamically downscaled products especially may be more appropriate when 587
spatially continuous projections are needed over larger regions with complex topography. For 588
example, where a large mountain range is associated with a strong precipitation gradient at sub-589
GCM gridbox scales, percentage changes in precipitation might also be expected to be more 590
spatially heterogeneous than in the New York Metropolitan Region.
591
Extreme event projections, so frequently sought by stakeholders for impact analysis, will 592
likely improve as statistical and dynamical downscaling evolve. RCMs especially hold promise 593
for assessing how ‘slow’ variations associated with climate change and variability will affect the 594
future distribution of ‘fast’ extremes like subdaily rainfall events. Nevertheless, translating RCM 595
simulations into stakeholder-relevant projections requires many of the same adjustments and 596
caveats described here for GCMs (such as bias correction). Statistical downscaling techniques 597
also hold promise as well for the simulation of extremes (non-stationarity not withstanding), to 598
the extent that predictor variables are well simulated by GCMs and linkable to policy relevant 599
local climate variables. Projections of extremes will also benefit from improved estimates of 600
historical extremes (such as the 1-in-100 year drought and coastal flood) as long-term tree ring 601
and sediment records (for example) are increasingly utilized.
602
There is also a need for improved simulation of climate variability at interannual to 603
decadal scales, as this is the time horizon for investment decisions and infrastructure lifetime in 604
many sectors, including telecommunications (NPCC, 2010). The limits to such predictability are 605
beginning to be explored in Coupled Model Intercomparison Project (CMIP5) experiments 606
initialized with observed ocean data, but this is a long-term research issue.
607
An absence of local climate projections need not preclude consideration of adaptation.
608
For many locales, climate changes in other regions may rival the importance of local changes by 609
influencing migration, trade, and ecosystem and human health, for example. Furthermore, some 610
hazards such as drought are often regional phenomena, with multi-state policy implications (such 611
as water-sharing agreements). Finally, since climate vulnerability depends on many non-climatic 612
factors (such as poverty), some adaptation strategies (such as poverty-reduction measures) can be 613
commenced in advance of climate projections.
614
Monitoring of climate indicators should be encouraged since it reduces uncertainties and 615
leads to refined projections. Locally, sustained high temporal resolution observation networks 616
can provide needed microclimatic information, including spatial and temporal variation in 617
extreme events such as convective rainfall and storm surge propagation. At the global scale, 618
monitoring of polar ice sheets and global sea level will improve understanding of sea level rise.
619
Periodic assessments of evolving climate, impacts and adaptation science will support 620
flexible/recursive adaptation strategies that minimize the impact of climate hazards while 621
maximizing societal benefits.
622 623
Acknowledgments
624
This work was supported by the Rockefeller Foundation. We acknowledge the modeling groups, 625
the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP for the 626
CMIP3 multi-model dataset, supported by the Office of Science, U.S. Department of Energy.
627
We thank Jonathan Gregory for additional GCM output not available from the WCRP dataset.
628
We also thank Adam Freed and Aaron Koch from the Mayor’s Office of Long Term Planning 629
and Sustainability, and Malcolm Bowman from the NPCC, for comments on prior work that 630
helped inform this paper. Finally with thank the anonymous reviewers of this manuscript.
631
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