Climate Modeling Univ. of Alaska-Fairbanks Summer School July 16, 2012

1. Climate Modeling Univ. of Alaska-Fairbanks Summer SchoolJuly 16, 2012 Joe Barsugli CIRES, University of Colorado, affiliated with NOAA/ESRL/PSD and Western Water Assessment

2. The Basic Narrative of Climate Change We are changing the Carbon Cycle Which causes an imbalance in the amount of energy reaching the Earth Which warms the planet Energy Balance Eventually feeding back on the carbon cycle Temperature Because the Energy Balance and the Water Cycle involve the winds and ocean currents -- they are changed too, potentially changing all aspects of climate, including clouds. Water vapor Which amplifies the climate change, mainly because of increased water vapor Which changes the water cycle

3. Goals • Climate Science • What is the basic narrative of climate change science?  • Emissions Scenarios • What is an emissions scenario? • Which emissions scenarios are used by climate modelers? • What are the main factors causing the Earth’s energy imbalance? • What are the important similarities and differences among the scenarios? • Climate Models • What is in a climate model? • What does the hydrologic component of a climate model look like? • What arethe basic methodologies of making climate model projections? • What are the known biases in climate model simulations? • What will the future of climate modeling bring?

4. Climate Change: The Usual Graphics 1850 1870 1890 1910 1930 1950 1970 1990 2010 CO2 emissions  accumulation of CO2 in atmosphere Pre-Industrial: 270 ppm2012: 393 ppm Warming of about 0.8C (1.5F) since pre-industrial climate Emissions from Fossil Fuel + Cement TOTAL ~9 Gt C/yr Data Source: G. Marland, T.A. Boden, R.J. Andres, and J. Gregg at CDIAC; Slide from global Carbon Project ; NOAA ESRL;

5. IPCC “SRES” Emissions Scenarios • IPCC Special Report on Emissions Scenarios http://www.ipcc.ch/ipccreports/sres/emission/index.htm • SRES scenarios… • Were developed because previous IPCC assessments did not make a clear distinction between uncertainty among climate model forecasts for a given scenario and uncertainty in the emissions trajectory itself. • “…are alternative images of how the future might unfold…” • Differ in their assumptions regarding “demographic development, socio-economic development, and technological change.” • Are given memorable names like “B1”, “A1B”, and “A2”

6. SRES (2000) CO2 Emissions Scenarios IPCC 5th assessment scenarios will probably include some scenarios with higher emissions.

7. Emissions Scenarios: Resulting CO2 Concentrations

8. Fossil Fuel Emissions: Actual vs. IPCC Scenarios Raupach et al 2007, PNAS (updated); Slide from global Carbon Project

9. RCPs, the new SRES scenarios • Stands for “Representative Concentration Pathways” • Derived from emissions scenarios much like SRES concentrations were… so that Earth System Models and “classic” Atmosphere Ocean GCMs could run comparable scenarios. • Are named by the approximate radiative forcing strength at the year 2100, in W/m^2. • Policy (mitigation) assumption are made (SRES did not make any policy assumptions)

10. RCPs are the new Emissions Scenarios RCP8.5 RCP6.0 RCP4.5 RCP2.6 Radiative Forcing: Imbalance in longwave + solar radiation caused by changes in greenhouse gas and aerosols relative to pre-industrial conditions. For comparison: Doubling CO2 ~ +4 W/m^2 Average insolation at top of the atmosphere ~ 340 W/m^2 Adapted from vanVuuren et al, 2011

11. RCP <-> SRES Note: different baselines used) Imbalance in radiation (solar + longwave (infrared) caused by changes in GHG relative to pre-industrial conditions For reference.. Doubling CO2 ~ +4 W/m^2 Average insolation ~ 340 W/m^2 vanVuuren et al, 2011

12. Which affects the Earth’s Energy Balance …

13. Radiative Forcing at Present (year 2005) Source: IPCC AR4 WG1 SPM

14. Global Mean Surface Air Temperature Projections (relative to 1980-1999) 2040 2070 Source: IPCC AR4 WG1 SPM

15. Bottom Line on Emissions Scenarios Scenarios are not forecasts, but can be useful planning tools. There are no probabilities associated with the occurrence of these scenarios. The emissions scenarios provide standard parameters for climate modelers to make intercomparison of their projections easier. Scenarios B1, A1B, and A2 have become de facto“low”, “medium” and “high” emissions scenarios for the CMIP3/AR4 models based on year 2100 cumulative emissions, and were chosen by climate modeling centers to intensively study. CMIP5 RCP’s are in many ways comparable to the SRES scenarios. RCPs 4.5 6.0 and 8.5 will probably be taken as the new “low” medium” and “high” scenarios. Earth System Models introduce another level of complexity and uncertainty. Radiative forcing in an Earth System model under a RCP emissions may differ from an AOGCM using the concentrations from the RCP

16. Bottom Line on (Global Mean) Climate Projections • The global mean climate responses to the different SRES scenarios start to diverge after about 2030. Before that, climate change is similar in all scenarios. By 2040 the scenarios have diverged somewhat. By 2070 B1 has appreciably diverged from the others. By 2100, all three scenarios are distinct. However the full estimate of uncertainty from all sources still has a large degree of overlap in year 2100. • For a given climate model, the temperature and precipitation changes in the different scenarios differ mainly in magnitude and timing. • For a given scenario, there is significant variation in temperature and precipitation changes among the different climate model simulations due to differences in model formulation. • CMIP5 – more divergence among radiative forcing, esp if you consider RCP2.6 and RCP8.5. In the latter half of the century there will be more divergence among simulations because of carbon-cycle feedbacks.

17. Choice (or not) of Emissions Scenarios The projected climate is contingent on the choice of emissions. Emissions scenario is not a big deal for regional climate out to mid-century. Emissions scenario becomes a big deal by the end of the century. Global Regional (UK) variability emissions emissions model model Fraction of uncertainty due to various sources Hawkins and Sutton, 2009

18. Review and Questions • Climate Science • What is the basic narrative of climate change science?  • How are we changing the carbon cycle?  • Emissions Scenarios • What is an emissions scenario?  • Which emissions scenarios are used by climate modelers?  • What are the main factors causing the Earth’s energy imbalance?  • What are the important similarities and differences among the SRES scenarios?  • Climate Models • What is in a climate model? • What does the hydrologic component of a climate model look like? • What is the basic methodology of making climate model projections? • What are the known biases in climate model simulations? • Why is downscaling necessary? • How do climate models differ in their projections for Western/Central US. • What will the future of climate modeling bring?

19. Breakout Exercise 1: Why use climate models to inform local decisions on adaptation? Break into groups, including instructors…. Your goal is to explain to a stakeholder why climate models are used to inform the future.

20. Components of Climate Models DYNAMIC VEGETATION ATMOSPHERIC CHEMISTRY LAND SURFACE (Physical) ATMOSPHERE LAND Biogeochemical Model (incl. Carbon Cycle) SEA ICE OCEAN OCEAN Atmosphere-Ocean GCM Earth System Model (ESM) • AOGCM – Atmosphere/Ocean General Circulation Model • Earth System Models • Problems with biases being passed from one component to another • IPCC Fifth Assessment is looking at both ESM and Atmosphere-Ocean GCMs

21. Numerical Solution: Timesteps and Gridboxes Solved at discrete time intervals: wind Typical grid size (resolution): Horizontal ~ 60-180 mi (100-300 km) Vertical ~30 layers of varying depth Typical timestep: 5 min - 20 min.

22. Model Topography: GCM vs. RCM Need for Downscaling! NCAR CCSM 3.0 T85 (1.4ox1.4o) feet WRF Regional Climate Model at 48km resolution feet

23. Some CMIP5 Atmosphere Model Resolutions AOGCM: NCAR CCSM4 (1.25 lon x 0.9 lat) MIROC4H (0.56X0.56; T213) MIROC5 (1.4X1.4) HadCM3(N48) (3.75 x 2.5) MRI CGCM3 (1.1X1.1) CNRM CM5 (1.4 X 1.4) (TL127) CANCM4 (2.8 X 2.8;T63) ESM: HadGEM-CC (1.875 x 1.25) GFDL ESM2M (2.5 x 2.0) MIROC-ESM-CHEM (2.8X2.8) Compare this to CMIP3, where the median model resolution was around 3-degrees, the highest (one) was at ~1 degree, and the next highest was NCAR CCSM3 was at 1.4 degrees. The coarsest were 4x5 degrees. Technical note: Take model resolutions with a small grain of salt. Resolution is not directly comparable between models with different numerical schemes.

24. Numerical Solution: Parameterization • Most of the physical process are at scales smaller than the grid spacing • Need to represent the cumulative efect of sub-gridscale processes on the grid in terms of gridscale parameters. • e.g. convective precipitation = f(T, moisture, winds) • Atmosphere • clouds: • precipitation & radiation • boundary layers • Surface fluxes, turbulent energy • Ocean • Mixing by eddies • Vertical mixing in upper ocean • Flow over sills => deep water formation • Based on theory and observations • Parameters “tuned” (usually globally) to get reasonable climate

25. Hydrologic Component of Land Surface Model NCAR CCSM3

26. Biophysical Component of Land Surface Model NCAR CCSM3

27. Hydrologic Component of Land Surface Model GFDL CM2.0

28. If the climate models have hydrology component in their land surface model, why do we need to run the output through another hydrology model? Hydrology in Climate Models • Each land surface model has a different formulation -- some simple, some sophisticated. • In even the most sophisticated global model, many (most?) topographic effects are lacking. • Sub-grid variations in land surface characteristics only partially represented. • Small basins not accounted for in routing models. • Land surface models in climate models have not (generally) been calibrated for specific regions.

29. Emissions Scenario (B1, A1B, A2; RCPs) GHG Concentrations (IAM) Climate Model (AOGCM) Earth System Model NCAR CCSM UKMO-HadCM3 NCAR CESM HadGEM Run1 … Run 4 Different numbers of runs for each model and scenario….

30. Climate Prediction vs. Projection • Climate Projections (IPCC AR4) • estimate the forced signal due to GHG and other radiative forcing agents. • are obtained from free-running GCM simulations that are not periodically restarted from observed conditions. • compare the climate “with” and “without” GHG emissions, etc.. • Climate (decadal) Prediction (to be Included in IPCC AR5) • Starts from the observed conditions (a process called data assimilation- very difficult for the ocean!) • Attempts to predict the actual trajectory of the decadal variations in climate • It is an open question how much skill there is on decadal time scales. Blue curve: free-running model Red blurs: ensemble of predictions for three starting dates. Black curve: observed Globally averaged annual mean surface temperature anomaly (relative to 1979–2001). Smith et al. 2007)

31. 20th Century Simulations Bias Correction Climate Model Projections Bias-corrected Projections Observations Modeling Methodology: Bias Correction is needed! • In many IPCC figures, a simple bias correction is (implicitly) applied: • Temperatures are shown relative to a reference climatology for each model. • Precipitation is shown as a percent change from a reference climatology for each model.

32. Resolution and Climate Model Evaluation Which spatial features of the climate do we expect the 20th century model simulations to reproduce, and which can’t they reproduce, even in theory? • CAN’T REPRODUCE EXACTLY • The climate statistics at a given observational site • The average climate in mountainous regions (snow may be badly misrepresented due to elevation effects) • The different climates in Denver, South Park, and Grand Junction • SHOULD REPRODUCE • The average regional climate for homogeneous regions. • The “large-scale” climate trends in mountainous regions • The qualitative changes in climate for broad regions as one crosses the Rockies.

33. Chaos and Climate Model Evaluation Which temporal features of the climate do we expect the 20th century model simulations to reproduce, and which can’t they reproduce, even in theory? • CAN’T REPRODUCE • The weather on May 4th, 1953, or any other given day. • The 1997-8 El Nino or other specific El Nino/La Nina Events • A specific warm decade or cold decade if it was due to internal modes of variability • SHOULD REPRODUCE • The climatological averages and other statistics for the observed record. • The statistics of El Nino/La Nina and other tropical disturbances such as the Madden-Julian Oscillation. • The statistics of internal decadal and multi-decadal climate fluctuations. Decadal or multi-decadal changes forced by volcanic, solar, and GHG variations.

34. Precipitation and Temperature Bias Percent precipitation error (a,b) and absolute temperature error (c,d) of the ensemble average of 18 models in relation to the University of Delaware climate data (1979- 99). Panels on the left show the mean error for May to October; panels on the right, November to April. (McAfee, Russell, and Webb, 2012 )

35. Koppen Classification bias (as a proxy for ecosystem types) OBS Model Bias-Adjusted Projection Model Projection .Köppen classification maps produced using (a) University of Delaware temperature and precipitation (1979-99), (b) the ensemble of 18 models (1979-99), using (c) adjusted 2079-99 ensemble climate and (d) unadjusted 2079-99 ensemble projections. See Table 1 for Köppen type descriptions. (McAfee, Russell and Webb, 2012)

36. Model Biases: Streamflow Green R. ? ??? Colorado R. ~72 BCM/yr (obs ~ 19) River routing model output: NCAR CCSM4.0 20th Century Average Only cells with > 5MAF/year are shown.

37. Model Biases: The Regional Picture WET COLD Source: IPCC WG1 Ch. 11, 11 Supp. And Climate Change in Colorado

38. ENSO Spectra in CMIP3 GCMs GFDL model ENSO 3x too strong NCAR CCSM has 2-year ENSO HADCM3 is close Gray band is obs. uncertainty.

39. Individual Model Projections: Temperature Source: IPCC WG1 11 Supp.

40. Individual Model Projections: Precipitation Source: IPCC WG1 Ch. 11 Supp.

41. Trends in Global Climate Modeling Increased Complexity -- more components of the Climate System (more sources of uncertainty!) Chemistry is expensive! 1990 1995 2001 Increased Resolution; but computational cost increases rapidly! We won’t be able to directly resolve cloud-scale processes (1km) on a global scale for long climate runs for quite a while. 2007

42. Trends in Global Climate Modeling • Neither of these guarantee that the model simulations will improve! We also need better scientific understanding… • Some personal opinions… • ENSO/Tropical Ocean changes -- I am optimistic based on increasing knowledge of oceanic processes . • MJO -- I am less optimistic, based on progress so far. • PDO and other decadal variability … problematic! • Mean climate biases over land -- increased resolution, better land surface models will help, but the remote influence of oceanic biases may remain.

43. Why use climate models – followupexercies • Take the reasons we all came up with and write a 1 paragraph statement about why(or why not) climate models are a useful source of information for adapation studies. [ we will leave the questions about downscaling for another day!]

44. End

45. Why Use Climate Models? To create plausible, physically based scenarios, reflecting the current state of scientific understanding, to inform planning for the future.