Changes in Weather and Climate Extremes and Their Causes. Xuebin Zhang (CRD/ASTD) Francis Zwiers (PCIC)

Transcription

1 Changes in Weather and Climate Extremes and Their Causes Xuebin Zhang (CRD/ASTD) Francis Zwiers (PCIC)

2 Outline What do we mean by climate extremes Changes in extreme temperature and precipitation Challenges to meet the end users needs Conclusions Page 2

3 IPCC AR4 Glossary An extreme weather event is an event that is rare at a particular place and time of year. Definitions of rare vary, but an extreme weather event would normally be as rare as or rarer than the 10th or 90th percentile of the observed probability density function. By definition, the characteristics of what is called extreme weather may vary from place to place in an absolute sense. Single extreme events cannot be simply and directly attributed to anthropogenic climate change, as there is always a finite chance the event in question might have occurred naturally. When a pattern of extreme weather persists for some time, such as a season, it may be classed as an extreme climate event, especially if it yields an average or total that is itself extreme (e.g., drought or heavy rainfall over a season) Page 3

4 What do we mean by extremes? Extreme events are easy to recognize but difficult to define, no unique definition for extreme Severe events can create large losses Rare events have a low probability of occurrence Extreme events generally have extreme values of certain important meteorological variables High-impact events are often associated with extreme weather and climate These terms have been used interchangeably Page 4

5 Attributes of extreme events relevant to impacts Rate of occurrence Magnitude (intensity) Temporal duration and timing Spatial scale (footprint) Multivariate dependencies Likelihood of when, where, and what extremes typically at regional and/or local scales Page 5

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7 Changes in temperature extremes Page 7

8 Indices of temperature extremes JJA warm days DJF Cold nights Alexander, Zhang, et al 2006

9 Kharin et al Page 9

10 Page 10

11 Fig. 1 Scaling factors and their 90% confidence intervals for annual extreme temperatures for 11 green, blue, pink error bars are for TNn, ALL and ANT forcings for period Page Red, TXn, TNx, and TXx respectively. Detection is claimed at the 10% significance level if the 90% confidence interval of a scaling factor is above zero line (Zwiers et al. 2011)

12 What we have learnt Increase in hot extremes and decrease in cold extremes Anthropogenic influence on extreme temperature detected at both global and regional scales Hot extremes projected to increase and cold extremes projected to decrease Model simulated changes in lowest night-time temperature too low and in highest day-time temperature too high when compared with observations Large gap in data coverage Page 12

13 Changes in precipitation extremes Page 13

14 Observed changes in extreme precipitation Page 14 Fig. 3.39

15 Projected future changes in extreme precipitation Page 15

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17 Extreme precipitation Observed 1-day or 5-day maximum precipitation ALL and ANT simulated by 5 GCMs GHG influence significantly contributed to the observed intensification of heavy precipitation events over large NH land areas during the latter half of the 20th century Models seem to under-simulate the observed increase in heavy precipitation Page 17 Min et al. (Feb. 17, Nature)

18 Event attribution Pall et al. (2011, Nature) Page 18

19 Zhang et al Page 19

20 What we have learnt There is likely an increase in extreme precipitation Anthropogenic influence on extreme daily precipitation might be detectable but simulated response may be smaller than observed changes. Anthropogenic influence may have increased risk of flood event such as the 2008 Autumn England flood. Extreme precipitation at the spatial scale of GCM grids is projected to increase in the future Small scale process generating daily extreme precipitation is lacking in a typical GCM setting Large-scale features such as ENSO, monsoons are important to extremes, but their future projection are quite uncertain There is larger gap in data coverage over the globe Page 20

21 Challenges Data for documenting past changes and model validation are lacking or unavailable. Observation for daily precipitation and temperature are sparse over most parts of the world, while observation for other important variables such as soil moisture that affect wide range extremes including drought and heat waves is very limited. Mismatch in spatial scales between observation (usually at the point scale) and model simulation (large area) hampers proper comparison between model simulation and observation. Models may lack the proper process to simulate the environment that generates extremes as observed such as extreme precipitation. Large-scale modes of variability such as ENSO exert strong influence on extremes, yet their future changes are uncertain. The knowledge and information on past changes and possible future changes in extremes falls short of user needs/expectations. Page 21

22 Conclusions Both extreme temperature and precipitation showed long-term trends in the past and the trends may be attributable to external forcing to various degree GCMs have projected changes in extremes into the future, but hardly in ready-to-use form and requires careful interpretation The bottom line: current monitoring and state of basic climate science don t meet the end use needs Page 22

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