Nansen Environmental and Remote Sensing Center Methods for diagnosing extreme climate events in gridded data sets D. J. Steinskog D. B. Stephenson, C. A. S. Coelho and C. A. T. Ferro Mines Paris, Fontainebleau, 20 March 2007

Nansen Environmental and Remote Sensing Center Page 2 Outline What are extremes in climate? Short info about R and RCLIM Methods for looking at extremes in gridded datasets Future development Conclusions

Nansen Environmental and Remote Sensing Center Climate extremes

Nansen Environmental and Remote Sensing Center Page 4 What is an extreme in meteorology? Large meteorological values –Maximum value (i.e. a local extremum) –Exceedance above a high threshold –Record breaker (threshold=max of past values) Rare event (e.g. less than 1 in 100 years – p=0.01) Large losses (severe or high-impact) (e.g. $200 billion if hurricane hits Miami) risk = p(hazard) x vulnerability x exposure

Nansen Environmental and Remote Sensing Center Page 5 Examples of wet and windy extremes Extra-tropical cyclone Hurricane Polar low Extra-tropical cyclone Convective severe storm

Nansen Environmental and Remote Sensing Center Page 6 Examples of dry and hot extremes Drought Wild fire Dust storm

Nansen Environmental and Remote Sensing Center Page 7 IPCC 2001 definitions Simple extremes: “individual local weather variables exceeding critical levels on a continuous scale” Complex extremes: “severe weather associated with particular climatic phenomena, often requiring a critical combination of variables” Extreme weather event: “An extreme weather event is an event that is rare within its statistical reference distribution at a particular place. Definitions of "rare" vary, but an extreme weather event would normally be as rare or rarer than the 10th or 90th percentile.” Extreme climate event: “an average of a number of weather events over a certain period of time which is itself extreme (e.g. rainfall over a season)”

Nansen Environmental and Remote Sensing Center Page 8 Future changes in extremes? IPCC 2001: Possible scenarios of extremes

Nansen Environmental and Remote Sensing Center R and RCLIM

Nansen Environmental and Remote Sensing Center Page 10 R – Short intro RCLIM make use of R, a powerful statistical tool. R is freely available, and can be used on most computer platforms It is a huge community working with and on R. R can be downloaded from

Nansen Environmental and Remote Sensing Center Page 11 RCLIM-initiative Part of Workpackage 4.3 ENSEMBLES: Understanding Extreme Weather and Climate Events Progress: –Spring 2005: Initiative started –March 2006: Delivery finished and methods made public –Future: More methods to be included, especially for daily datasets.

Nansen Environmental and Remote Sensing Center Page 12 RCLIM-initiative Main motivation –Climate analysis requires increasingly good statistical analysis tools. Aims –Develop statistical methods and write user friendly functions in the R language for describing and exploring weather and climate extremes in gridded datasets, making efficient use of the already existing packages. Webpage –

Nansen Environmental and Remote Sensing Center Page 13 RCLIM-initiative The RCLIM initiative will develop functions for: –Reading and writing netcdf gridded datasets –Exploratory climate analysis in gridded datasets –Climate analysis of extremes in gridded datasets –Animating and plotting climate analysis of gridded datasets Team: –David Stephenson, Caio Coelho, Chris Ferro and Dag Johan Steinskog

Nansen Environmental and Remote Sensing Center Statistical methods

Nansen Environmental and Remote Sensing Center Page 15 European heat wave 2003 Estimated total mortality: Effects on crops, both negative and positive This extreme wheather was caused by an anti- cyclone firmly anchored over the western European land mass holding back the rain- bearing depressions that usually enter the continent from the Atlantic ocean. This situation was exceptional in the extended length of time (over 20 days) during which it conveyed very hot dry air up from south of the Mediterranean event can be used as an analog of future summers in coming decades (Beniston, GRL 2004) It is very likely (confidence level >90%) that human influence has at least doubled the risk of a heatwave exceeding this threshold magnitude (Stott et.al., Nature 2004)

Nansen Environmental and Remote Sensing Center Page 16 Data used in this presentation Monthly mean gridded surface temperature (HadCRUT2v) 5 degree resolution January 1870 to December 2005 Summer months only: June July August Grid points with >50% missing values and SH are omitted. –Special focus on the 2003 summer heat wave in Europe

Nansen Environmental and Remote Sensing Center Page 17 Mean temperature Central Europe (12.5ºE, 47.5ºN)

Nansen Environmental and Remote Sensing Center Page 18 Standard Deviation

Nansen Environmental and Remote Sensing Center Page 19 For sufficiently large thresholds, the distribution of values above a sufficiently large threshold u approximates the Generalized Pareto Distribution (GPD): Model for tails: peaks-over- threshold Shape = -0.4 – upper cutoff Shape = 0.0 – exponential tail Shape = 10 – power law tail Probability density function

Nansen Environmental and Remote Sensing Center Page 20 Example: Central England Temperature n = 3082 values Min = -3.1C Max = 19.7C 90 th quantile: 15.6C

Nansen Environmental and Remote Sensing Center Page 21 Location parameter: u=15.6C Maximum likelihood estimates: Scale parameter: /- 0.09C Shape parameter: /- 0.04C  Upper limit estimate: GPD fit to values above 15.6C

Nansen Environmental and Remote Sensing Center time series of summer (June-July-August) monthly mean temperatures for a grid point in Central Europe (12.5ºE, 47.5ºN) = 15.2ºC 75 th quantile (u y,m = 16.2ºC) 2003 exceedance Excess (T y,m – u y,m ) Long term trend (L y,m )

Nansen Environmental and Remote Sensing Center Page 23 Time varying threshold JJA pts & trend+seasonal terms Excesses  Flexible approach that gives exceedances 25% of months

Nansen Environmental and Remote Sensing Center Page 24 Time mean of 75% threshold

Nansen Environmental and Remote Sensing Center Page 25 Mean of the excesses  Large over extra-tropical land regions

Nansen Environmental and Remote Sensing Center Page 26 GPD scale parameter estimate  Large over extra-tropical land regions

Nansen Environmental and Remote Sensing Center Page 27 GPD shape parameter estimate Generally negative  finite upper temperature limit

Nansen Environmental and Remote Sensing Center Page 28 Upper limit for excesses  Largest over high-latitude land regions

Nansen Environmental and Remote Sensing Center Page 29 Return periods for August 2003 event  Central Europe return period of 133 years (c.f. Schar et al years!)

Nansen Environmental and Remote Sensing Center Page 30 The role of large-scale modes  ENSO effect on temperature extremes in NH

Nansen Environmental and Remote Sensing Center Page 31 Teleconnections between extremes

Nansen Environmental and Remote Sensing Center Page 32 1-point association map for extreme events  association with extremes in subtropical Atlantic

Nansen Environmental and Remote Sensing Center Page 33 Future development of RCLIM and methods Methods for data with high temporal correlation will be introduced (e.g. daily dataset) Quantile regression to estimate the thresholds? Improve the plotting procedure – filled contours and projections Feedback on other methods that could be included is wanted!

Nansen Environmental and Remote Sensing Center Page 34 Conclusions Huge potential of doing extremes on gridded datasets Simple extremes can be analysed using peaks-over-threshold methods Extremes do not have a unique definition Future work include testing the methods on daily datasets and develop new methods for data with high autocorrelation with special focus on Arctic region

Nansen Environmental and Remote Sensing Center Page 35 Reference Coelho, C. A. S., C. A. T. Ferro, D. B. Stephenson and D. J. Steinskog; Exploratory tools for the analysis of extreme weather and climate events in gridded datasets, Submitted to Journal of Climate Contact info: David Stephenson, Dag Johan Steinskog,

Nansen Environmental and Remote Sensing Center Page 36 Thank you for your attention!