Presentation human impact on droughts and hot temperatures
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Why do we see more and more heat waves (hot temperature extremes)? How is soil moisture related to these hot temperatures? How are temperatures going to change over Eastern China, and over the rest of the world? How many people will be affected by extremely hot summers in the future? References Mueller, B., X. Zhang , and F.W. Zwiers (2016): Historically hottest summers projected to be the norm for more than half of the world's population within 20 years, Environmental Research Letters.Climatic Change. Mueller, B., and X. Zhang (2016): Causes of drying trends in northern hemispheric land areas in reconstructed soil moisture data, Climatic Change. Seneviratne, S.I., M.G. Donat, B. Mueller, and L.V. Alexander (2014): No pause in the increase of hot temperature extremes. Nature Climate Change, 4, 161-163, doi:10.1038/nclimate2145.
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Presentation human impact on droughts and hot temperatures
- 1. Introduction Preliminary results Outlook Supplement. figures 1 Human influence on droughts and temperature extremes Brigitte Mueller brigitte.mueller@yahoo.ca
- 2. Introduction Preliminary results Outlook Supplement. figures 2 Outline 1. Why study extremes? 2. Soil moisture impacts on hot extremes (coupling) 3. Human influence on changes in soil moisture 4. Human influence on hot future summers 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers
- 3. Introduction Preliminary results Outlook Supplement. figures 3 Motivation Heat waves impact on health and economy Heat waves and droughts impact agricultural output Hot extremes more relevant than mean temperatures 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Year Country Estimated death toll (No. of people) Estimated costs Reference 2003 Europe 70’000 13 billion Euros a,b 2010 Russia 55’736 5-12 billion Euros c,d 2012 US 123 31 billion USD e 2013 Eastern China 40 59 billion RMB f,g a. Robine et al., Com. Ren.Biol., 2008 . b. www.metoffice.gov.uk c. www.emdat.be d. www.dw.de e. www.ncdc.noaa.gov f. www.news.xinhuanet.com g. Hou et al., Met. Mon., 2014
- 4. Introduction Preliminary results Outlook Supplement. figures 4 Motivation: Increase in hot extremes 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Cold Average Hot Probabilitydensity Temperature distribution
- 5. Introduction Preliminary results Outlook Supplement. figures 5 Motivation: Increase in hot extremes Similar to a Figure from IPCC 2007 and 2013 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Cold Average Hot Probabilitydensity Temperature distribution New climate
- 6. Introduction Preliminary results Outlook Supplement. figures 6 Motivation: Increase in hot extremes Seneviratne, Donat, Mueller and Alexander (Nature Climate Change), 2014 Increase in extremely hot global land temperature vs. increase in mean temperature Trends 1997 - 2012 Extreme T Mean T Temperatureanomaly[K] K per 10 years 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Txp95 over land─ Tmean over land Tmean ocean + land ─ ──
- 7. Introduction Preliminary results Outlook Supplement. figures 7 Precipitation Evaporation Temperature/ extreme T Runoff Soil moisture Greenhouse Gases Floods Importance of soil moisture Agricultural production 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers
- 8. Introduction Preliminary results Outlook Supplement. figures 8 Importance of soil moisture 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Precipitation Evaporation Temperature/ extreme T Runoff Soil moisture Greenhouse Gases Agricultural productionFloods www.newscientist.org www.oklahomafarmreports.com
- 9. Introduction Preliminary results Outlook Supplement. figures 9 Outline 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers 1. Why study extremes? 2. Soil moisture impacts on hot extremes (coupling) 3. Human influence on changes in soil moisture 4. Human influence on hot future summers
- 10. Introduction Preliminary results Outlook Supplement. figures 10 Land water and energy balances Seneviratne et al. (Earth-Sci. Reviews), 2010 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Water balance ΔS = P – E – Rs - Rg Energy balance ΔH = Rn – λE – SH – G Variables S: Soil water storage P: Precipitation E: Evapotranspi- ration R: Runoff H: Ground heat storage SW: Shortwave radiation LW: Longwave radiation λE: Latent heat of vaporization * Evaporation SH: Sensible heat flux G: Ground heat flux Rn: Net radiation
- 11. Introduction Preliminary results Outlook Supplement. figures 11 Land – atmosphere feedback Wet soils Dry soils Alexander (Nature Geoscience), 2011 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers
- 12. Introduction Preliminary results Outlook Supplement. figures 12 Land – atmosphere feedback 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Seneviratne et al. (Earth-Sci. Reviews), 2010Alexander (Nature Geoscience), 2011
- 13. Introduction Preliminary results Outlook Supplement. figures 13 Mueller and Seneviratne (PNAS), 2012 Land-atmosphere coupling with observations Correlation: Number of hot days and preceding drought index 1979-2010 Hatched areas significant at 10% level White: Not defined Hot days at hottest month of each year Drought index before that month: Precipitation deficit over 3 months (standardized precipitation index SPI) 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Hot day Temperature > 1 wet -0.99 to 0.99 near normal < -1 dry
- 14. Introduction Preliminary results Outlook Supplement. figures 14 % of years Probability after dry conditions After wet conditions Occurrence of above average # of hot days Mueller and Seneviratne (PNAS), 2012 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers
- 15. Introduction Preliminary results Outlook Supplement. figures 15 Mueller and Seneviratne (GRL), 2014 Evaporation bias (annual) Land-surface impact on temperature in models CMIP5 minus reference data Too wet 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Temperature bias (annual) Too cold
- 16. Introduction Preliminary results Outlook Supplement. figures 16 Mueller and Seneviratne (GRL), 2014 Evaporation bias (annual) Land-surface impact on temperature in models Mueller et al., (HESS), 2013 Evapotranspiration (40 datasets), 1989-1995 CMIP5 minus reference data Too wet 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Temperature bias (annual) Too cold
- 17. Introduction Preliminary results Outlook Supplement. figures 17 Outline 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers 1. Why study extremes? 2. Soil moisture impacts on hot extremes (coupling) 3. Human influence on changes in soil moisture 4. Human influence on hot future summers
- 18. Introduction Preliminary results Outlook Supplement. figures 18 Changes in the water cycle Precipitation Evaporation Temperature/ extreme T Runoff Soil moisture Greenhouse Gases Agricultural productionFloods www.newscientist.org www.oklahomafarmreports.com 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers
- 19. Introduction Preliminary results Outlook Supplement. figures 19 Changes in the water cycle 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Seneviratne et al. (Earth-Sci. Reviews), 2010 Water balance ΔS = P – E – Rs - Rg
- 20. Introduction Preliminary results Outlook Supplement. figures 20 Changes in the water cycle 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Dots: Robust. Hatching: Not significant (rel. to internal climate variability) RCP8.5 scenario (2081- 2100 minus 1986-2005) Sedláček and Knutti (Environ. Res. Lett), 2014 Future minus past
- 21. Introduction Preliminary results Outlook Supplement. figures 21 1) Are changes in soil moisture and droughts distinguishable from internal climate variability? → Detection 2) Are changes due to human influence? → Attribution Changes in droughts 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers
- 22. Introduction Preliminary results Outlook Supplement. figures 22 Central line of evidence that has supported statements such as …’most of the observed increase in global average temperature since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations’ IPCC AR5, Chapter 10, 2013 Detection and Attribution 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers
- 23. Introduction Preliminary results Outlook Supplement. figures 23 Detection and Attribution 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Figure from NOAA NCDC / CICS-NC 1. Detection Observed variable
- 24. Introduction Preliminary results Outlook Supplement. figures 24 Detection and Attribution 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Figure from NOAA NCDC / CICS-NC 1. Detection Internal variability: Variability due to natural internal processes (ENSO, PDO etc.) Observed variable
- 25. Introduction Preliminary results Outlook Supplement. figures 25 Detection and Attribution 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Figure from NOAA NCDC / CICS-NC Natural forcings Anthropogenic forcings 1. Detection 2. Attribution
- 26. Introduction Preliminary results Outlook Supplement. figures 26 Attribution Observations 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers - Greenhouse gases - Anthropogenic aerosols - Volcanic aerosols - Solar variability - Internal variability Climate model Hypotheses: Anthropogenic (ANT)? Natural (NAT)? Pictures right top and bottom from NOAA
- 27. Introduction Preliminary results Outlook Supplement. figures 27 Detection and Attribution 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers 1986 - 1996 XY Y = X × β + ε Adapted from Weaver and Zwiers (Nature), 2000 1946 -1956 ˆ ˆ Evaluate amplitude estimates Evaluate goodness of fit Observations Model simulations
- 28. Introduction Preliminary results Outlook Supplement. figures 28 Detection and Attribution 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers 1986 - 1996 XY Y = X × β + ε Adapted from Weaver and Zwiers (Nature), 2000 1946 -1956 Evaluate amplitude estimates Evaluate goodness of fit Observations Model simulations Signal detected in observations 0ˆ β=(XT C-1X) -1XTC-1Y
- 29. Introduction Preliminary results Outlook Supplement. figures 29 Warm season soil moisture trends 1951-2005 Adapted from: Mueller and Zhang, submitted to Climatic Change ALL NAT OBS Obs NAT ALL Soil moisture Drought area 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Region: Northern mid-latitudes Observations Natural + Anthropogenic Only natural forcing
- 30. Introduction Preliminary results Outlook Supplement. figures 30 Scaling factors β Adapted from: Mueller and Zhang, submitted to Climatic Change Detected if > 0 Bars: 5-95% confidence intervals Soil moisture Drought area ALL NAT ALL NAT 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers ALL = natural and anthropogenic forcing NAT = only natural
- 31. Introduction Preliminary results Outlook Supplement. figures 31 Outline 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers 1. Why study extremes? 2. Soil moisture impacts on hot extremes (coupling) 3. Human influence on changes in soil moisture 4. Human influence on hot future summers
- 32. Introduction Preliminary results Outlook Supplement. figures 32 Hot temperatures observed in the past showed strong negative impacts (health, economy, agriculture) How likely does a summer as hot or hotter than the hottest in the past become in the future? Hot future summer: Motivation 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Year Country Estimated death toll (No. of people) Estimated costs Ref 2003 Europe 70’000 13 billion Euros a,b 2010 Russia 55’736 5-12 billion Euros c,d 2012 US 123 31 billion USD e 2013 Eastern China 40 59 billion RMB f,g Heat waves, droughts
- 33. Introduction Preliminary results Outlook Supplement. figures 33 Regions for summer land temperature 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers
- 34. Introduction Preliminary results Outlook Supplement. figures 34 Summer temperature Eastern Asia CMIP5 simulations: ALL = anthropogenic and natural forcing (54 runs) NAT = only natural forcing (45 runs) RCP4.5 and 8.5 = representative concentration pathway (15 runs) Observations: CRU Temperatureanomaly[K] 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers
- 35. Introduction Preliminary results Outlook Supplement. figures 35 Representative Concentration Pathways 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Data obtained from http://www.pik-potsdam.de/~mmalte/rcps/ Emissions CO2 Concentrations 0 5 10 15 20 25 30 35 2000 2020 2040 2060 2080 2100 CO2-Emissions(GtC/yr) RCP8.5 RCP4.5 0 200 400 600 800 1000 1200 1400 2000 2020 2040 2060 2080 2100 CO2-EqConcentration(ppm) RCP8.5 RCP4.5
- 36. Introduction Preliminary results Outlook Supplement. figures 36 Summer temperature Eastern Asia Historical hottest year 2010: 0.94 K above climatology How much more likely is a 2010 summer in the future? Temperatureanomaly[K] 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers
- 37. Introduction Preliminary results Outlook Supplement. figures 37 Constraining simulations with observations 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Adjust past and future simulations 1. In detection and attribution framework, calculate factor βALL by which simulations need to be scaled to match best the observations 2. Adjust simulations with scaling factors βALL Xadjusted = XALL*βALL where X is the ensemble mean of the simulations. 3. Add internal variability to obtain a set of possible temperatures Xrec = XALL*βALL + Controlsimulationsindividual where Controlsimulations are 390 samples of internal variability.
- 38. Introduction Preliminary results Outlook Supplement. figures 38 Exceedance probabilities Exceedanceprobabilityin% Probability of exceeding maximum historic temperature (Eastern Asia) 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Results consistent with Sun et al. (Nat Clim Change), 2014 for Eastern China
- 39. Introduction Preliminary results Outlook Supplement. figures 39 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Regions for summer land temperature
- 40. Introduction Preliminary results Outlook Supplement. figures 40 Percentage of population experiencing 1 in 2 summers hotter than the past maximum ─ under RCP4.5 ─ under RCP8.5 Hottest historical summers will be the norm for more than half of the world's population by 2035 Population affected by hot summers Mueller, Zhang and Zwiers, in preparation 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Percentofworldpopulation[%]
- 41. Introduction Preliminary results Outlook Supplement. figures 41 Percentage of population experiencing 1 in 2 summers hotter than the past maximum ─ under RCP4.5 ─ under RCP8.5 Hottest historical summers will be the norm for more than half of the world's population by 2035 Population affected by hot summers Mueller, Zhang and Zwiers, in preparation 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Percentofworldpopulation[%]
- 42. Introduction Preliminary results Outlook Supplement. figures 42 Hottest historical summers will be the norm for more than half of the world's population by 2035 Population affected by hot summers Mueller, Zhang and Zwiers, in preparation 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Percentage of population experiencing 9 in 10 summers hotter than the past maximum ─ under RCP4.5 ─ under RCP8.5 Percentofworldpopulation[%]
- 43. Introduction Preliminary results Outlook Supplement. figures 43 Hot temperatures strong increase in the last 30 years Soil moisture influences temperature extremes in large fraction of the globe Potential for seasonal prediction Summary I. # hot days and drought 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers
- 44. Introduction Preliminary results Outlook Supplement. figures 44 Northern hemispheric land areas have become drier Human influence on changes in soil moisture are significant Strong increase in probabilities for future hot summers after 2020, especially without climate action Summary II. 1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers Soil moisture
- 45. Introduction Preliminary results Outlook Supplement. figures 45 Local versus large scale effect on droughts, evaporation and temperature Improving simulations of temperature selecting models with good representation of land-atmosphere coupling Response of global water cycle to increasing greenhouse gases Regional climate change Open questions brigitte.mueller@env.ethz.ch
- 46. Introduction Preliminary results Outlook Supplement. figures 46 Local versus large scale effect on droughts, evaporation and temperature Improving simulations of temperature selecting models with good representation of land-atmosphere coupling Response of global water cycle to increasing greenhouse gases Regional climate change Thank you brigitte.mueller@env.ethz.ch
- 47. Introduction Preliminary results Outlook Supplement. figures 47 New info updated after presentation Papers mentioned are now published: Mueller, B., X. Zhang , and F.W. Zwiers (2016): Historically hottest summers projected to be the norm for more than half of the world's population within 20 years, Environmental Research Letters.Climatic Change. Link Mueller, B., and X. Zhang (2016): Causes of drying trends in northern hemispheric land areas in reconstructed soil moisture data, Climatic Change. Link Supplementary
- 48. Introduction Preliminary results Outlook Supplement. figures 48 Short explanation of detection and attribution algorithm used in the presented analyses Supplementary
- 49. Introduction Preliminary results Outlook Supplement. figures 49 Total least square model Y = β*X + ϵ with X=X0 + ϵX and Y=Y0 + ϵY β=(XT C-1X) -1XTC-1Y C covariance of ϵ, internal variability C estimated from control simulations C Y =λCX : Assumption tested with residual consistency test
- 50. Introduction Preliminary results Outlook Supplement. figures 50 Assumption: XANT = XALL - XNAT Y = βANT*XANT + βNAT*XNAT + ε Two-signal detection