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Temperature and Precipitation Projections for the Mobile Bay Region

Appendix

Indicator Definitions

Secondary climate change indicators requested for the greater Mobile Bay region. Unless otherwise indicated, all values are calculated individually for each weather station, for 1980-2009 using both observations and historical simulations, and for the periods 2010-2039, 2040-2069 and 2070-2099 using future simulations.

1. Timeseries of annual average precipitation, maximum, mean, and minimum temperature from 1960 to 2099.

2. Monthly 30-year mean of precipitation, maximum, mean, and minimum temperature

3. Seasonal 30-year mean of precipitation, maximum, mean, and minimum temperature

4. Annual 30-year mean of precipitation, maximum, mean, and minimum temperature

5. Seasonal and annual 30-year average number of days and maximum number of consecutive days with maximum daily temperature >=95F, >100F,105F,110F

6. Annual 30-year mean of 4 consecutive warmest days in summer and coldest days in winter: 5th, 25th, 50th ,75th, 95th percentile

7. Annual coldest day and maximum 7-day average temperature with the % probability (1,5,10,50) of occurrence during 30-year period

8. Annual precipitation for 24-h period with a 0.2, 1, 2, 5, 10, 20, 50 % occurrence during 30-year period

9. Annual two and four-day exceedance probability across 2 consecutive days :0,2, 1,2, 5, 10, 20, 50 percentile and mean (note that these are calculated differently than the variable in #8 above).

10. Seasonal 30-year mean of largest 3-day total precipitation in each season

Dealing with Low-Frequency Quantiles

For certain variables that are sampling beyond the range of the observed historical distribution (e.g., #8 and 9), the 0.2% and 1% exceedences are identical. This is because the distributions are only based on 30 values for each period. On average, creating a distribution from only 30 points means that there will only be one value above 95% and below 5%. So anything above 95% or below 5% is not robust, as this requires extrapolating far beyond the original data used to create the distribution.

The function used here to fit quantiles uses an empirical distribution based on the data, not a theoretical distribution. More information on this routine can be found here: http://stat.ethz.ch/R-manual/R-devel/library/stats/html/quantile.html

However, engineers often use a Log-Pearson distribution to fit precipitation curves. This distribution is theoretical rather than empirical, which means it can extrapolate beyond the ranges of the data used to derive the distribution. For that reason, we asked: what difference would it make if a Log-Pearson fit were used to calculate the quantiles of the distribution?

For the quantiles contained within the range of the data, an empirical distribution is more accurate than fitting a theoretical distribution because it makes no assumptions regarding the distribution of the data. For these quantiles, differences between the two approaches would be a function of how well the theoretical distribution fit the empirical distribution.

For quantiles that lie beyond the range of the data (for example, the 1st or 99th quantiles in a dataset that is made up of less than 99 data points), there is a significant difference between the two approaches. An empirical approach simply assigns an out-of-range quantile the most extreme value on that side of the distribution. So, for example, if the highest value in a distribution of 20 points were 42.5 then the value of 90th quantile and any higher quantile would all be set to 42.5. This method provides a highly constrained estimate of extreme values as it does not allow estimates beyond the range of the data used to derive the distribution. A theoretical distribution, on the other hand, provides some estimate of the shape of the tail beyond the values used to make the distribution. Quantile values outside the range of the data points can then be estimated based on that distribution. Using a theoretical distribution therefore provides an extended estimate of extreme values as it permits estimates beyond the range of observed (or modeled) data.

Since the empirical approach was used to derive the quantiles in this analysis, they should be viewed as minimum estimates for these values. In reality, the values of quantiles beyond the range of the observations used to derive the distribution will be more extreme than the values given here.

Indicator Robustness

Concerns about the robustness of multiple variables were addressed by re-defining certain precipitation variables so as to sample from a greater part of the distribution. This analysis found that:

  1. Any difference between the 3 scenarios is insignificant so averaging across all scenarios for precipitation extremes is recommended if the calculations sample from only 30 points.
  2. General trends (or lack thereof) appear relatively robust for variable #7.

The "general drop-off" in precipitation towards the end of the century originates directly from the projections from global climate models. Maps showing projected precipitation changes across the entire Southeast have been added to this report to place projections for the Mobile Bay area into the context of the larger geographic context. The general trends are for a decrease in summer precipitation balanced by an increase in fall and winter. Decreases become slightly stronger under higher emissions (annual average changes for A1fi: -6%, A2: -3%) compared to lower (annual average changes for B1: +2%).

References

Andres, R.J., D.J. Fielding, G. Marland, T.A. Boden, and N. Kumar. 1999. Carbon dioxide emissions from fossil-fuel use, 1751-1950. Tellus 51B:759-65

Collins, W.D., et al., 2006. The Community Climate System Model: CCSM3. J. Clim., 19, 2122-2143.

Delworth, T., et al., 2006. GFDL's CM2 global coupled climate models - Part 1: Formulation and simulation characteristics. J. Clim., 19, 643- 674.

Dettinger, M. D., D. R. Cayan, M. K. Meyer, and A. E. Jeton. 2004. "Simulated hydrologic responses to climate variations and change in the Merced, Carson, and American River basins, Sierra Nevada, California, 1900-2099". Climatic Change 62, 283-317

Durre, I., M. J. Menne, and R. S. Vose, 2008. Strategies for evaluating quality-control procedures. Journal of Climate and Applied Meteorology, 47, 1785-1791.

Flato, G.M., 2005. The Third Generation Coupled Global Climate Model (CGCM3) (and included links to the description of the AGCM3 atmospheric model). http://www.cccma.bc.ec.gc.ca/models/cgcm3.shtml.

Foster, G. and S. Rahmstorf. 2011. Global temperature evolution 1979-2010. Environmental Research Letters, 6, 044022.

Furevik, T., et al., 2003. Description and evaluation of the Bergen climate model: ARPEGE coupled with MICOM. Clim. Dyn., 21, 27-51.

Gillett, N., V. Arora, G. Flato, J. Scinocca and K. von Salzen. 2012. Improved constraints on 21st-century warming derived using 160 years of temperature observations. Geophysical Research Letters, 39, L017074.

Gordon, H.B., et al., 2002. The CSIRO Mk3 Climate System Model. CSIRO Atmospheric Research Technical Paper No. 60, Commonwealth Scientific and Industrial Research Organisation Atmospheric Research, Aspendale, Victoria, Australia, 130 pp, http://www.cmar.csiro.au/e-print/ open/gordon_2002a.pdf.

Grotch, S. & M. MacCracken. 1991. The use of general circulation models to predict regional climatic change. Journal of Climate, 4, 286-303/

Hawkins & Sutton, 2009. 'The potential to narrow uncertainty in regional climate predictions', BAMS, 90, p1095

Hawkins & Sutton, 2011. 'The potential to narrow uncertainty in projections of regional precipitation change', Climate Dynamics, 37, 407-418

Hayhoe et al. 2004. Emission pathways, climate change, and impacts on California. PNAS 101 12423-12427

Hayhoe, K., C. Faris, J. Rogula, M. Aufhammer, N. Miller, J. VanDorn and D. Wuebbles. 2010(b). An integrated framework for quantifying and valuing climate change impacts on urban energy and infrastructure: A Chicago case study. Journal of Great Lakes Research, 36(sp2), 94-105.

Hayhoe, K., C. Wake, B. Anderson, X.-Z. Liang, E. Maurer, J. Zhu, J. Bradbury, A. DeGaetano, A. Hertel, and D. Wuebbles. 2008. Regional Climate Change Projections for the Northeast U.S., Mitigation and Adaptation Strategies for Global Change, 13, 425-436

Hayhoe, K., J. vanDorn, D. Wuebbles, K. Cherkauer, and N. Schlegal. 2010(a). Regional climate change projections for Chicago and the Great Lakes. Journal of Great Lakes Research, 36(sp2), 7-21.

Hayhoe, K., S. Sheridan, S. Greene and L. Kalkstein. 2010(c). Climate change, heat waves, and mortality projections for Chicago. Journal of Great Lakes Research, 36(sp2).

Hegerl, G.C., F. W. Zwiers, P. Braconnot, N.P. Gillett, Y. Luo, J.A. Marengo Orsini, N. Nicholls, J.E. Penner and P.A. Stott, 2007. Understanding and Attributing Climate Change. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Huber, M. and R. Knutti. 2011. Anthropogenic and natural warming inferred from changes in Earth's energy balance. Nature Geoscience, doi 101.1038/NGEO1327

IPCC, 2007. Summary for Policymakers. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Knutti, R. 2010. The end of model democracy? Climatic Change 102, 395-404

Knutti, R. and G. Hegerl. 2008. The equilibrium sensitivity of the Earth's temperature to radiation changes. Nature Geoscience, 1, 735-743.

Koenker, R. and G. Bassett. 1978. Regression Quantiles, Econometrica, 46, 33-50.Z

Koenker, R. and K. Hallock, 2001. Quantile Regression. Journal of Economic Perspectives, 15, 143-156.

Kostopoulou, E., C. Giannakopoulos, C. Anagnostopolou, K. Tolika, P. Maheras, M. Vafiadis and D. Founda. 2007. Simulating maximum and minimum temperature over Greece: A comparison of three downscaling techniques. Theor. Appl. Clim., 90, 65-82.Z

Lüthi, D., M. Le Floch, B. Bereiter, T. Blunier, J-M Barnola, U. Siegenthaler, D. Raynaud, J. Jouzel, H. Fischer, K. Kawamura & T. Stocker. 2008. High-resolution carbon dioxide concentration record 650,000-800,000 years before present. Nature 453, 379-382

Maurer, E. P. and H. Hidalgo. 2008. Utility of daily vs. monthly large-scale climate data: An intercomparison of two statistical downscaling methods, Hydrology and Earth System Sciences, 12(2) 551-563

Meehl, G. A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. F. B. Mitchell, R. J. Stouffer, and K. E. Taylor, 2007. The WCRP CMIP3 multi-model dataset: A new era in climate change research, Bulletin of the American Meteorological Society, 88, 1383-1394.

Meinhausen, M., et al. 2006. Multi-gas emissions pathways to meet climate targets. Climatic Change 75: 151-194.

Moss, R.H. et al. 2010. The next generation of scenarios for climate change research and assessment. Nature 463: 747-756.

Myhre, G., K. Alterskjaer, D. Lowe 2009. A fast method for updating global fossil fuel carbon dioxide emissions. Environmental Research Letters 4, doi 10.1088/1748-9326/4/3/034012

Nakicenovic, N., et al. 2000. Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios. Cambridge University Press, Cambridge, U.K.

North American Regional Climate Change Assessment Program. http://www.narccap.ucar.edu/

Overland, James E., Muyin Wang, Nicholas A. Bond, John E. Walsh, Vladimir M. Kattsov, William L. Chapman, 2011: Considerations in the Selection of Global Climate Models for Regional Climate Projections: The Arctic as a Case Study. J. Climate, 24, 1583-1597.

Randall, D.A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, 2007. Cilmate Models and Their Evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Raupach, M. R., Marland, G., Ciais, P., Le Quere, C., Canadell, J. G., Klepper, G., et al. 2007. Global and regional drivers of accelerating CO2 emissions. Proceedings of the National Academy of Sciences of the United States of America, 104(24), 10288-10293.

Roeckner, E., et al., 2003. The Atmospheric General Circulation Model ECHAM5. Part I: Model Description. MPI Report 349, Max Planck Institute for Meteorology, Hamburg, Germany, 127 pp.

Salas-Mélia, D. and Coauthors, 2005. Description and validation of the CNRM-CM3 global coupled model. CNRM Working note 103, 36 pp.

Stern, D.I., and R.K. Kaufmann. 1998. Annual Estimates of Global Anthropogenic Methane Emissions: 1860-1994. Trends Online: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A. doi: 10.3334/CDIAC/tge.001

Stoner, A., K. Hayhoe, X. Yang and D. Wuebbles. 2011. An Asynchronous Regional Regression Model to Downscale Daily Temperature and Precipitation Climate Projections. Intl. J. Climatology, submitted.

Stott, P. & J. Kettleborough. 2002. Origins and estimates of uncertainty in predictions of twenty-first century temperature rise. Nature, 416, 723-725

Tebaldi, C., K. Hayhoe, J. Arblaster and G. Meehl. 2006. Going to the extremes: An intercomparison of model simulated historical and future changes in extreme events. Climatic Change, 79, 185-211.

Trenberth, K.E., P.D. Jones, P. Ambenje, R. Bojariu, D. Easterling, A. Klein Tank, D. Parker, F. Rahimzadeh, J.A. Renwick, M. Rusticucci, B. Soden and P. Zhai, 2007. Observations: Surface and Atmospheric Climate Change. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

U.S. Global Change Research Program (USGCRP, 2000) U.S. National Assessment of the Potential Consequences of Climate Variability and Change. Cambridge University Press. Available online at: http://www.globalchange.gov/usimpacts/

U.S. Global Change Research Program (USGCRP, 2009) Global Climate Change Impacts in the United States. Cambridge University Press. Available online at: http://www.globalchange.gov/usimpacts/

Vose, R. S., Richard L. Schmoyer, Peter M. Steurer, Thomas C. Peterson, Richard Heim, Thomas R. Karl, and J. Eischeid 1992. The Global Historical Climatology Network: long-term monthly temperature, precipitation, sea level pressure, and station pressure data. Oak Ridge, Tennessee: Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory.

Vrac, M., M. Stein, X. Liang and K. Hayhoe 2007. A general method for validating statistical downscaling methods under future climate change, Geophysical Research Letters, 34(18).

Washington, W.M., et al., 2000. Parallel Climate Model (PCM) control and transient simulations. Clim. Dyn., 16, 755-774.

Weigel, A., R. Knutti, M. Liniger and C. Appenzeller. 2010. Risks of model weighting in multimodel climate projections. Journal of Climate, 23, 4175-4191

Wuebbles, D., K. Hayhoe and J. Parzen. 2010. Assessing the effects of climate change on Chicago and the Great Lakes. Journal of Great Lakes Research, 36(sp2), 1-6.

Updated: 03/27/2014
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