Methods for Climate Scenario Planning and Downscaling

Transcription

1 Methods for Climate Scenario Planning and Downscaling Cynthia Rosenzweig 1 and Radley Horton 2 1 NASA Goddard Institute for Space Studies 2 Columbia University Environment, Energy Security and Sustainability Symposium May 10, 2011 New Orleans, LA

2 Stakeholder Questions How do weather and climate impact my system? Direct climate model variables (e.g. surface temperature, precipitation) Integrated variables (e.g. drought severity index) What level of precision/confidence is needed? Most likely result vs. full model based distribution Temporal and spatial coverage Temporal and spatial resolution Indirect effects Climate changes in other regions Indirect impacts of climate changes Interaction of climate impacts with other societal changes, including population growth and mitigation activities

3 Hot Summer of 2010* Summer was hottest on record in New York City Daily maximum temperature anomaly ( F) July 2010 Central Park, NY July was the 2 nd warmest month on record at Central Park, with average temperature of 81.3 F (July 99 was the warmest 81.4 F) There were 16 days at or above 90 F, the normal for July is 6. In July 2009, the highest maximum temperature for the month was 86 F August 2010 Central Park, NY Mean temperature in August was 2.3 F above normal monthly average of 75.1 F Summer was hottest on record by 0.5 F At Central Park, there were 37 days at or above 90 F, tied for the 2 nd most on record Daily maximum temperature anomaly ( F) Mean number of days at or above 90 F per year for the 2050s is projected to be 38 days *Teachable moment about climate vulnerability 3

4 Downscaling Products and Techniques Single Global Climate Model gridbox approach Delta method applied to station data (NYC Panel on Climate Change) Statistical downscaling--bias-correction Spatial-Disaggregation (BCSD; Maurer, et al. 2007, based on Wood et al. 2002, 2004, and Maurer 2007) Direct use of monthly time series of T and P for impact models For daily projections at station level, we use modified delta method and random sampling Dynamical downscaling--regional Climate Model (RCM) simulations from the North American Regional Climate Change Assessment Program archive (NARCCAP; Mearns et al. 2009, EOS) Changes in frequency and duration of key extremes Changes in (intra-annual) variability more generally Delta method

5 NPCC Climate Scenarios Regional climate projections are based on 16 GCMs (7 GCMs for sea level) and 3 emissions scenarios Model output for the single gridbox covering New York City is used Future changes are presented for time slices relative to the baseline period ( for sea level) Time slices are 30 year periods (10 for sea level) centered around a given decade, for example, the 2050s is Model-based probability The combination of GCMS and emissions scenarios produce 48 outputs for temperature and precipitation For each scenario time period and variable, the results constitute a model-based probability function Bias-corrected, spatially downscaled output from the 16 GCMs now being used (Maurer, 2007) Frequency distribution of model based temperature changes for the 2050s relative to the base period.

6 Downscaling Products and Techniques Single GCM gridbox approach Delta method applied to station data (NYC Panel on Climate Change) Statistical downscaling--bias-correction Spatial-Disaggregation (BCSD; Maurer, et al. 2007, based on Wood et al. 2002, 2004, and Maurer 2007) Direct use of monthly time series of T and P for impact models For daily projections at station level, we use modified delta method and random sampling Dynamical downscaling--regional Climate Model (RCM) simulations from the North American Regional Climate Change Assessment Program archive (NARCCAP; Mearns et al. 2009, EOS) Changes in frequency and duration of key extremes Changes in (intra-annual) variability more generally Delta method

7 Statistical Downscaling Bias Correction Spatial Disaggregation (BCSD) Method--Steps Bias correction Regrid 1/8º observations and GCM outputs to 2º resolution Generate monthly cumulative distribution functions at 2º, for (observations, the baseline GCM, and the future simulation) Quantile map the baseline GCM onto the observed data (note: this preserves higher order statistics) (BCSD; Maurer, et al. 2007)

8 Statistical Downscaling Bias Correction Spatial Disaggregation (BCSD) Method--Steps Spatial Downscaling Rank each future month based on baseline GCM CDF, then replace with observed data as above Generate factor field (temperature differences, precipitation ratios) Interpolate these factors to 1/8º Apply the downscaled factors to the observed 1/8º data (for temperature this is addition; for precipitation this is multiplication (BCSD; Maurer, et al. 2007)

9 Statistically Downscaled Regional Temperature Projections GCM GCM A1B Annual Temperature ( F) A1B 2080s 16 GCM Annual Temperature Change ( F), relative to NASA ARC NASA ARC NASA CASI Bias-corrected and spatially downscaled CMIP3 data: Maurer et al (2007).

10 Statistically Downscaled Regional Precipitation Projections GCM A1B Annual Annual Precipitation (in) A1B 2080s 16 GCM Annual Precipitation Change (%), relative to NASA ARC NASA ARC NASA CASI Bias-corrected and spatially downscaled CMIP3 data: Maurer et al (2007).

11 Statistically Downscaled Extreme Event Projections ARC Daily Temperatures Location Baseline 2020s 2050s 2080s Max temperature at or above 90 F (days/year) Min temperature at or below 40 F (days/year) Livermore to to to 104 Moffett Field 9 10 to to to 29 Livermore to to to 63 Moffett Field to to 19 5 to 14 The number of days per year exceeding 90 F is projected to rise in the coming century, and the number of days with temperatures below 40 F is projected to decrease. NASA CASI

12 Statistical Downscaling--Summary Pros Computationally inexpensive Wide range of spatial scales Cons Limited by quality of observed climate data Assumes stationarity : future statistical relationships will be the same as historical relationships Multivariate analyses (e.g. temperature and precipitation) can be challenging Types Bias corrected, spatial downscaled technique (BCSD) Wood et al., 2004; Maurer et al., 2007 Constructed analogues Maurer and Hidalgo, 2008 Weather generators

13 Downscaling Products and Techniques Single GCM gridbox approach Delta method applied to station data (NYC Panel on Climate Change) Statistical downscaling--bias-correction Spatial-Disaggregation (BCSD; Maurer, et al. 2007, based on Wood et al. 2002, 2004, and Maurer 2007) Direct use of monthly time series of T and P for impact models For daily projections at station level, we use modified delta method and random sampling Dynamical downscaling--regional Climate Model (RCM) simulations from the North American Regional Climate Change Assessment Program archive (NARCCAP; Mearns et al. 2009, EOS) Changes in frequency and duration of key extremes Changes in (intra-annual) variability more generally Delta method

14 Dynamical Downscaling As part of the North American Regional Climate Change Assessment Program (NARCCAP), RCMs were run at 50km resolution for three experiments: NCEP Reanalysis-driven, December 1979-November 2000: How well do the RCMs simulate observations over the NE, when driven by perfect boundary conditions?* GCM hindcast-driven, December 1970-November 2000 How sensitive are the RCM results to biases * in the driving GCMs?* GCM future-driven, December 2041-November 2070, A2 SRES Scenario How does the forcing associated with greenhouse gases and other radiatively important agents manifest itself at more local scales?* *Several caveats here

15 Annual Precipitation (in./day), How well do the RCMs simulate observed mean precipitation over the NE, when driven by perfect boundary conditions?

16 SRES A2 2050s minus 1980s annual temperature ( F) BCSD Projections--Mean Annual Changes

17 SRES A2 2050s divided by 1980s annual precipitation (%) BCSD Projections--Mean Annual Changes

18 Projections--Mean Changes NYC Gridbox (# of simulations) GCM (16) BCSD (16) NARCCAP (4) Mean Annual Temperature Change ( F) 2.5 to 6.1 (4.1) 2.9 to 5.7 (4.2) 4.3 to 5.9 (4.6) Mean Annual Precipitation Change (%) -9 to 10 (5) -6 to 23 (5) 0 to 14 (5) SRES A2-driven 2050s divided by 1980s GCM hindcast-driven

19 Projections--% Change in # days with >.5 in prcp SRES A2-driven 2050s divided by 1980s GCM hindcast-driven

20 SRES A2 2050s divided by 1980s, standard deviation of annual temperature ( F) BCSD Projections--Interannual Variability

21 Dynamical Downscaling--Summary Pros Physics/process-based future relationships allowed to evolve as the climate system evolves Wide range of variables can be analyzed, and analyzed together Cons Computationally expensive Multidecadal simulations only available from a subset of GCMs, emissions scenarios, and timeslices Results are highly dependent on quality of driving GCM/boundary conditions Can be challenging to discern relative role of the GCM and RCM in producing a result Large scale feedbacks may be missed Cannot provide point data

22 Conclusions and Future Work Conclusions The range of results associated with downscaling technique is often smaller than other sources of uncertainty that influence decisions, such as: Global climate sensitivity Emergent patterns not captured by global climate models Interannual to decadal variability Climate impacts Socio-economic changes Next Steps Analyze pre-conditions for extreme precipitation events in GCMs and RCMs Explore standardized statistically downscaled daily products (CMIP3 and CMIP5) such as BCSD, Bias Corrected Constructed Analogues (BCCA; Maurer and Hidalgo, 2008) Develop localized downscaling tailored to sector-specific stakeholder needs Statistical Downscaling Model (e.g., SDSM; Wilby et al. 2002) - Stochastic approaches, weather generators Neiman et al. 2008, Jour. of Hydromet. Snow depth at Wanakena, NY based on SDSM downscaling. (Tryhorn and Degaetano in NYState CliMAID Report, to be released in 2011)

23 Stakeholder Questions How do weather and climate impact my system? Direct climate model variables (e.g. surface temperature, precipitation) Integrated variables (e.g. drought severity index) What level of precision/confidence is needed? Most likely result vs. full model based distribution Temporal and spatial coverage Temporal and spatial resolution Indirect effects Climate changes in other regions Indirect impacts of climate changes Interaction of climate impacts with other societal changes, including population growth and mitigation activities

24 Quantitative Climate Scenarios--Questions How many GCMs, and initializations? Which GCMs (weighting?), and why? How many initializations? Which emissions scenarios? Which generation of GCMs? (Coupled Model Intercomparison Project 3 vs. 5)

25 Climate Change Scenario Planning, Global Business Network for National Park Service Eastern Forests and Urban Landscapes Workshop, December 7-9, 2010

26 PlaNYC Socioeconomic New York City s population is expected to grow to 9.1 million persons by 2030 Projections By 2030, the employment force will grow by 750,000 jobs Electricity and heating fuel consumption are also projected to increase Source: PLANYC

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28 Probabilistic Regional Climate Change (Greene, Goddard and Lall, 2006, J. Climate) (IPCC (IPCC AR4, AR4, WG1, WG1, Chp Chp 11, 11, Fig Fig 11.26) 11.26)

29 Regional Patterns of Climate Change 20 th Century: Observed 20 th Century: patterns of Observed T & P trends agree patterns better of with T & models P trends that have more agree realistic better patterns with of SST models trends. that have more realistic patterns of SST trends. (Shin and Sardeshmukh, 2010, Climate Dynamics)

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31 Sustainability Plan for NYC The plan features 10 goals Create more homes for almost a million more New Yorkers, while making housing affordable and sustainable housing Ensure that all New York live within a 10- minute walk of a park Clean up all contaminated land Open 90% of waterways for recreation by reducing water pollution and natural area preservation Develop critical back-up systems for water network Improve travel times by adding transit capacity Reach a full state of good repair on roads, subways and rails Provide cleaner and more reliable power Achieve the cleanest air of any big city in America Reduce global warming emissions by more than 30% PlaNYC is New York City s plan for a sustainable future (April, 2007) All linked to climate change mitigation and adaptation Available online at:

32 NYC Stakeholder Engagement Process Mayor or City Official High-Level Buy-In NPCC - Climate Change Adaptation in New York City: Building a Risk Management Response City-wide Sustainability Office Coordinating Role Stakeholders include: - City Agencies - Regional Authorities - Private Stakeholders Stakeholder Task Force E D C A B Expert Panel Expert Knowledge: - Climate Change Scientists - Legal experts - Insurance experts Integration across Sector-specific Working Groups Source: NPCC, 2010

33 Developing Climate Risk Information Process used to develop climate risk factors for New York City

34 Key Points for NCA Put context first add changing climate risks to ongoing policy planning processes Aka: Start with trajectories of population, GDP, poverty, etc. and of multiple stresses, e.g., on ecosystems Communicate that climate scenario process is interactive and ongoing make sure that stakeholders know that projections will be updated Clarify and characterize major known risks as well as uncertainties; quantitatively and qualitatively Monitor and evaluate adaptation decisions, including use of scenarios!

35 Climate Hazard Information Process used to develop climate risk factors for New York City Projection Range, based on 16 GCMs and 3 SRES scenarios Time slices based on single GCM gridbox, delta method approach Key thresholds Number of days below 32 F (transportation sector) Number of days above 90 F (energy and health sectors) Number of intense precipitation events (e.g.,.5 in./day; water sector) Qualitative projections

36 CCRUN Prototype Projects: Climate Historical analysis of multi-century trends and variability Statistically and dynamically downscaled projections at seasonal to multi-decadal timescales Extreme events Sea level rise projections Stakeholder-driven analysis and presentation

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