by Judith Curry
This post discusses Workshop presentations on broadening the portfolio of climate information for use in regional adaptation decisions.
This post is a follow-on to the four previous posts:
- UK-US Workshop on Climate Science to Support Robust Adaptation Decisions
- UK-US Workshop Part II: Perspectives from the private sector on climate adaptation
- UK-US Workshop Part III: Strategies for robust decision making for climate adaptation
- UK-US Workshop Part IV: Limits of climate models for adaptation decision making
Projections of future climate variability and change on decadal (nominally 10-40 years from now) and regional scales are of high relevance for decision making. However, these time and space scales are particularly challenging for climate models because natural variability can be at least as large as forced climate variability.
Broadening the portfolio of climate information used to support decision making is envisioned to integrate the following elements:
- Understanding of climate model limitations on regional/decadal scales
- Mixed portfolio of climate information (data including historical and paleo; models – both dynamical and empirical; natural climate variability)
- Putting climate information into perspective with other issues
This post highlights two presentations that relate to broadening the portfolio of climate information to support decision making:
- Judith Curry – Generating possibility distributions of scenarios for regional climate change
- Rob Wilby – Climate ‘hot spot’ analysis to inform adaptation planning in Yemen
Development agencies can face difficult decisions about where and how to prioritise investments in climate risk reduction measures. Community level adaptation options in semi-arid regions include rainwater harvesting, terrace rehabilitation, re-afforestation, wadi bank protection, irrigation schemes, small dams, training village-level agriculture technicians (extension workers/ veterinarians), establishment of saving and credit groups and provision of small grants for income generating activities, and literacy training. Implementation is especially challenging in data sparse regions where there may be few meteorological stations, complex topography and extreme weather phenomena. This paper describes the development and application of a decision-support tool to identify ‘hot spots’ of climate risk and to guide adaptation activities in Yemen. The project was commissioned by the United Nations International Fund for Agricultural Development (IFAD) in order to identify ~500 village units that are vulnerable to flash flooding, soil erosion, water scarcity and reduced crop potential. We blended surface meteorological observations, remotely sensed (TRMM and NDVI) data, physiographic indices, and regression techniques to produce gridded maps of annual mean precipitation and temperature, as well as parameters for site-specific, daily weather generation for any location in Yemen. Climate sensitivity analysis was then applied to the impact models alongside socio-economic criteria to identify communities that are potentially most at risk. Finally, a Google Earth tool was provided to enable field officers to locate communities and interpret climate risks within a wider landscape context.
At timescales beyond a season, available ensembles of climate models do not provide the basis for probabilistic predictions of regional climate change. Given the uncertainties, the best that can be hoped for is scenarios of future change that bound the actual change, with some sense of the likelihood of the individual scenarios. This talk argues that this is a realistic expectation for timescales out to 2040 or 2050.
Scenario thinking – Scenarios are provocative and plausible accounts of how the future might unfold. The purpose is not to identify the most likely future, but to create a map of uncertainty of the forces driving us toward the unknown future. Scenarios help decision makers order and frame their thinking about the long-term while providing them with the tools and confidence to take action in the short-term.
Are GCMs the best tool? – GCMs may not be the best tool, and are certainly not the only tool, for generating scenarios of future regional climate change. Current GCMs inadequate for simulating natural internal variability on multidecadal time scales. Computational expense precludes adequate ensemble size. GCMs currently have little skill in simulating regional climate variations. Dynamical & statistical downscaling adds little value, beyond accounting for local effects on surface variables. Further, the CMIP5 simulations only explore various scenarios of emissions, they do not explore multiple scenarios of solar forcing.
Possibility theory provides a useful framework for considering scenarios. Possibility theory is an imprecise probability theory that states that any hypothesis not known to be impossible cannot be ruled out. A possibility distribution distinguishes what is plausible versus the normal course of things versus surprising versus impossible.
The challenge for identifying an upper bound for future scenarios is to identify the possible and plausible worst case scenarios. What scenarios would be genuinely catastrophic? What are possible/plausible time scales for the scenarios? Can we ‘falsify’ these scenarios for the timescale of interest based upon our background knowledge of natural plus anthropogenic climate change?
On decadal timescales, the scenarios of greatest interest involve extreme weather events. The decadal scenarios are not time series, but rather frequencies of extreme events (including clusters) and worst case scenarios over the target time interval: floods, droughts, heat waves, tropical cyclones, heavy snowfalls, etc.
- Warm AMO (shifting towards neg), cool PDO
- More La Nina events
- Decrease/flattening of the warming trend
Weather/climate impacts for U.S. (analogue: 1955-1965):
- more rainfall in NW, mid Atlantic states
- less rainfall in W/SW, Texas
- more hurricane landfalls along Atlantic coast, fewer in FL
“Spiking” from AGW:
- Increasing rainfall
- Increase in hurricane intensity
- Overall reduced hurricane landfall owing to eastward extension of the Atlantic warm pool
Scenarios (quantitative guidance) 2015-2025
- Generate a large synthetic climatology of the event using Monte Carlo resampling from the pdfs conditioned on the particular regime.
- Utilize extreme value theory to extrapolate the historical data into a far tail region so as to be able to simulate more extremes.
- Identification of ‘clustering’ of events, both intraseasonal and over periods of 3 years or less. Extreme events can arise from a single extreme storm, a correlated series of smaller events, or from antecedent conditions
Likelihoods for the possibility distributioncan be developed by:
- Weighting preference for scenario generation method
- Historical precedent
- Expert judgment
- Number of independent paths for reaching a particular scenario event
GCMs are not the only, or best, way to generate future scenarios of regional climate change
On decadal time scales, the greatest vulnerability is to extreme weather events: scenarios of frequency (clustering), worst case
Climate science in support of developing empirical approaches for scenario development:
- Improved regional historical and paleo records of extreme events
- Improved statistical methods for analyzing extreme events
- Improved understanding of the climate dynamics of extreme events and natural variability of regional climate
- Scenario discovering using a broader range of approaches