1. Kevin E Trenberth NCAR Kevin E Trenberth NCAR Observations of climate change Help!

2. Global Warming is unequivocal Since 1970, rise in:Decrease in:  Global surface temperatures NH Snow extent  Tropospheric temperatures Arctic sea ice  Global SSTs, ocean Ts Glaciers  Global sea level Cold temperatures  Water vapor  Rainfall intensity  Precipitation extratropics  Hurricane intensity  Drought  Extreme high temperatures  Heat waves Since 1970, rise in:Decrease in:  Global surface temperatures NH Snow extent  Tropospheric temperatures Arctic sea ice  Global SSTs, ocean Ts Glaciers  Global sea level Cold temperatures  Water vapor  Rainfall intensity  Precipitation extratropics  Hurricane intensity  Drought  Extreme high temperatures  Heat waves IPCC 2007

3. The climate is changing. We can and should take mitigating actions that will slow and eventually stop climate change. Meanwhile we must adapt to climate change. But adapt to what? We do not have predictions. We do not have adequate reliable observations. We do not have the needed information system! The climate is changing. We can and should take mitigating actions that will slow and eventually stop climate change. Meanwhile we must adapt to climate change. But adapt to what? We do not have predictions. We do not have adequate reliable observations. We do not have the needed information system!

4. Global mean temperatures are rising faster with time 150 0.045  0.012 100 0.074  0.018 50 0.128  0.026 25 0.177  0.052 Warmest 12 years: 1998,2005,2003,2002,2004,2006, 2001,1997,1995,1999,1990,2000 Period Rate Years  /decade IPCC 2007

5. Extreme Heat Wave Summer 2003 Europe 30,000 deaths Extreme Heat Wave Summer 2003 Europe 30,000 deaths Heat waves are increasing: an example Trend plus variability? IPCC 2007

6. Surface Temperature 1901-2005 Surface Temperature 1901-2005 It has not warmed uniformly: More warming over land Why no warming over SE USA? Or N Atlantic It has not warmed uniformly: More warming over land Why no warming over SE USA? Or N Atlantic IPCC 2007

7. The most important spatial pattern (top) of the monthly Palmer Drought Severity Index (PDSI) for 1900 to 2002. The time series (below) accounts for most of the trend in PDSI. Drought is increasing most places Mainly decrease in rain over land in tropics and subtropics, but enhanced by increased atmospheric demand with warming IPCC 2007

8. Extremes of temperature are changing! Observed trends (days) per decade for 1951 to 2003: 5 th or 95 th percentiles From Alexander et al. (2006) Extremes of temperature are changing! Observed trends (days) per decade for 1951 to 2003: 5 th or 95 th percentiles From Alexander et al. (2006) IPCC 2007

9. Absence of warming by day coincides with wetter and cloudier conditions Drought Increases in rainfall and cloud counter warming Trend in Warm Days 1951-2003 IPCC 2007

10. Regional climate change Hypothesis: It is impossible to address regional climate change without fully addressing how patterns of climate variability (modes) change, and thus how: ENSO: El Niño Southern Oscillation NAO/NAM: North Atlantic Oscillation/Northern Annular Mode SAM: Southern Annular Mode PDO: Pacific Decadal Oscillation AMO: Atlantic Multidecadal Oscillation change! Regional climate change Hypothesis: It is impossible to address regional climate change without fully addressing how patterns of climate variability (modes) change, and thus how: ENSO: El Niño Southern Oscillation NAO/NAM: North Atlantic Oscillation/Northern Annular Mode SAM: Southern Annular Mode PDO: Pacific Decadal Oscillation AMO: Atlantic Multidecadal Oscillation change!

11. El Niño - Southern Oscillation SLPSurface temperature Precipitation IPCC 2007 Cooler Wetter

12. Pacific Decadal Oscillation SST pattern (above) and time series (lower right) of 1 st EOF of N Pacific SSTs. NPI index of Aleutian Low Indian Ocean SST (tropics) 1976/77 climate shift IPCC 2007

13. Many observed climate anomalies can be simulated in models with specified SSTs Sahel drought: Hurrell et al 2004, Giannini et al 2003, Hoerling, US Dust Bowl: Schubert et al. 2004, Seager et al. 2005 Drought (US, Europe, Asia): Hoerling and Kumar 2003 But we can not (yet) simulate the observed SSTs. Sahel drought: Hurrell et al 2004, Giannini et al 2003, Hoerling, US Dust Bowl: Schubert et al. 2004, Seager et al. 2005 Drought (US, Europe, Asia): Hoerling and Kumar 2003 But we can not (yet) simulate the observed SSTs.

14. Global increases in SST are not uniform. Why?  Coupling with atmosphere  Tropical Indian Ocean has warmed to be competitive as warmest part of global ocean.  Tropical Pacific gets relief owing to ENSO?  Deeper mixing in Atlantic, THC. This pattern is NOT well simulated by coupled models! Relates to ocean uptake of heat, heat content & transport. Global increases in SST are not uniform. Why?  Coupling with atmosphere  Tropical Indian Ocean has warmed to be competitive as warmest part of global ocean.  Tropical Pacific gets relief owing to ENSO?  Deeper mixing in Atlantic, THC. This pattern is NOT well simulated by coupled models! Relates to ocean uptake of heat, heat content & transport. IPCC 2007

15. IPCC experience on observations  Sorting out the climate signal from the noise in inadequate observations from a changing observing system is an ongoing continual challenge  Space-based observations are a particular challenge  Sorting out the climate signal from the noise in inadequate observations from a changing observing system is an ongoing continual challenge  Space-based observations are a particular challenge

16. Annual anomalies of maximum and minimum temperatures and diurnal temperature range (DTR) (°C) averaged for the 71% of global land areas for 1950 to 2004. DTR 1979-2004 Annual anomalies of maximum and minimum temperatures and diurnal temperature range (DTR) (°C) averaged for the 71% of global land areas for 1950 to 2004. DTR 1979-2004 Issues: 1.Missing data and treatment 2.Quality control 3.Max and Min T much more sensitive to inhomogeneities 4. Urban heat island 5. Need to continue to pressure countries to provide high frequency data (hourly and daily) Temperatures IPCC 2007

17. Radiation Top-of Atmosphere: Wielicki et al. 2002 1.Published Science 2.Revised following comment 3.Edition 2 (orbit decay correction) 4.Edition 3 (SW filter dome) 1.Published Science 2.Revised following comment 3.Edition 2 (orbit decay correction) 4.Edition 3 (SW filter dome) Is this shift real? IPCC 2007

18. Precipitation: not a continuous variable Large differences in amounts. Inability to analyze characteristics: intensity, frequency, duration, type, as well as amount. Need hourly data!

19. Tropical rainfall 30N-30S LandTotal Ocean Tropical rainfall 30N-30S LandTotal Ocean Land: systematic offset 3% Ocean: no relationship Total: dominated by ocean Land: systematic offset 3% Ocean: no relationship Total: dominated by ocean Issues: Need much more complete and better data on all hydrological variables set in a holistic framework: Precipitation: hourly (intensity, frequency, duration, type, amount); streamflow, runoff, evaporation, drought indices, soil moisture (incl ice), snow cover depth…

20. N. Atlantic hurricane record best after 1944 with aircraft surveillance. Global number and percentage of intense hurricanes is increasing North Atlantic hurricanes have increased with SSTs SST (1944-2005) Marked increase after 1994

21. Some issues: Partial reprocessing of ISCCP data has occurred for tropical storms (Kossin) Records are far from homogeneous, even for satellite era Records/practices are not comparable in different regions, even now. We desperately need an internationally coordinated reprocessing of all satellite data for hurricanes, to get many parameters of interest, such as size, intensity, rainfall, integrated variables (0-100 km; 0-400 km) etc. Ivan 2004

22. Main Issues The in situ data are not global and have problems Satellites drift in orbit and instruments degrade: the data generally do not provide a climate record. They could. The satellite record is in jeopardy, especially from demanifesting several climate instruments from NPOESS. A baseline transfer standard is essential: in situ super sites (reference radiosonde plus network). Regional climate requires attention to modes of variability and model initialization The in situ data are not global and have problems Satellites drift in orbit and instruments degrade: the data generally do not provide a climate record. They could. The satellite record is in jeopardy, especially from demanifesting several climate instruments from NPOESS. A baseline transfer standard is essential: in situ super sites (reference radiosonde plus network). Regional climate requires attention to modes of variability and model initialization

23. Why do we need an integrated Earth System Analysis? We have a lot of observations: from satellites and other remote sensing. The volumes are huge We use but a small fraction Most are not climate quality Inconsistencies exist across variables They do not make a climate observing system Reprocessing and reanalysis must be part of system We have a lot of observations: from satellites and other remote sensing. The volumes are huge We use but a small fraction Most are not climate quality Inconsistencies exist across variables They do not make a climate observing system Reprocessing and reanalysis must be part of system Goal: Climate Data Records

24. 1.There is a need to better come to grips with the continually changing observing system. 2.There is no baseline network to anchor the analyses or space observations. The radiosonde network is not it! 3.The challenge is to improve continuity and be able to relate a current set of observations to those taken 20 years ago (or in the future). 4.There is a need for more attention to data synthesis, reprocessing, analysis and re-analysis of existing data sets; and 5. There must be a baseline set of measurements:  Sparse network (30-40) of “reference sondes” for satellite calibration and climate monitoring, UT water vapor; co-located with regular sonde sites to replace them at appropriate times; integrated with ozone sondes and/or GAW and BSRN = GRUAN?  GPS Radio Occultation. 1.There is a need to better come to grips with the continually changing observing system. 2.There is no baseline network to anchor the analyses or space observations. The radiosonde network is not it! 3.The challenge is to improve continuity and be able to relate a current set of observations to those taken 20 years ago (or in the future). 4.There is a need for more attention to data synthesis, reprocessing, analysis and re-analysis of existing data sets; and 5. There must be a baseline set of measurements:  Sparse network (30-40) of “reference sondes” for satellite calibration and climate monitoring, UT water vapor; co-located with regular sonde sites to replace them at appropriate times; integrated with ozone sondes and/or GAW and BSRN = GRUAN?  GPS Radio Occultation.

25. The challenge is to better determine: 1)how the climate system is changing 2)how the forcings are changing 3)how these relate to each other (incl. feedbacks) 4)attribution of anomalies to causes 5)what they mean for the immediate and more distant future (assessment) 6)Validate and improve models 7)seamless predictions on multiple time scales 8)how to use this information for informed planning and decision making 9)how to manage the data and reanalyze it routinely 10)how to disseminate products around the world 11)how to interact with users and stakeholders and add regional value From Trenberth et al 2002 The challenge is to better determine: 1)how the climate system is changing 2)how the forcings are changing 3)how these relate to each other (incl. feedbacks) 4)attribution of anomalies to causes 5)what they mean for the immediate and more distant future (assessment) 6)Validate and improve models 7)seamless predictions on multiple time scales 8)how to use this information for informed planning and decision making 9)how to manage the data and reanalyze it routinely 10)how to disseminate products around the world 11)how to interact with users and stakeholders and add regional value From Trenberth et al 2002       