Climate modeling: 1) Why? 2) How? 3) What?

Transcription

1 Climate modeling: 1) Why? 2) How? 3) What? Matthew Widlansky 1) Why model the climate? Hawaii Fiji Sachs and Myhrvold: A Shifting Band of Rain 1

2 Evidence of Past Climate Change? Mean annual climatology Rainfall (Intertropical Convergence Zone & South Pacific Convergence Zone) Sachs et al. 2009, Nature Geoscience 2

3 Mean annual climatology Sea surface temperature (West Pacific warm pool) NOAA s Climate Prediction Center Mean annual climatology Sea surface height (Why are sea levels higher in the west?) Satellite measured 3

4 We are interested in departures from Normal Anomaly = Observation Climatology For example, Anomaly Feb 2016 = Observation Feb 2016 Climatology Feb That is, If a month is warmer than normal, that month s anomaly is positive If a month is cooler than normal, that month s anomaly is negative El Niño: Warmer sea surface temperatures in equatorial eastern Pacific 4

5 El Niño: Trade winds weaken (or reverse) El Niño: Extreme sea levels (low and high stands) Guam 5

6 El Niño: Shifting rainfall bands W/m 2 Outgoing Longwave Radiation (thermal) emitted from Earth and atmosphere (clouds) out to space Why do cloud tops emit less? El Niño Southern Oscillation (ENSO) ENSO Blog features/blogs/enso 6

7 Sea surface temperature drives global atmospheric circulation Warm air rises, like a hot air balloon Ocean & Atmosphere are coupled: For example, warmer Sea Surface Temperature anomalies cause larger wind anomalies (weaker Trade Winds) which cause further warming. Positive feedback ( chain reaction ) = Bigger event 7

8 Biggest El Niño since 1998 Sea surface height (satellite measured) Measuring ENSO Sea surface temperature index Trenberth and Fasullo 2013, Earth s Future 8

9 El Niño will likely end soon, La Niña may follow Climate model forecast Hawaii s winter drought was well predicted Climate model forecast from August for Dec Feb Annamalai et al. 2015, AsiaPacific Issues Normal rainfall likely by summer 9

10 2) How is the climate simulated? Principals of physics, chemistry, biology incorporated into a mathematical model of climate Variables Temperature? Precipitation? Humidity? Wind? Clouds? Ice? Many computations are need! We need to solve for values of the variables described by equations (e.g., conservation of mass, momentum, and heat) over time. But, the equations can not be solved analytically so they must be discretized in time and space. Supercomputers like the Yellowstone machine (National Center for Atmospheric Research) 10

11 Cost of a simple climate model 1.5 Trillion Calculations takes only a couple hours on a laptop! MIT OpenCourseWare Global Climate Change Main drivers of Climate Change IPCC Fifth Assessment Report Figure 1 Radiative balance between incoming solar shortwave radiation (SWR) and outgoing longwave radiation (OLR) 11

12 Representative Concentration Pathway (RCP) Scenarios: How much greenhouse warming? What causes the wiggles? IPCC Fifth Assessment Report 3) What do climate models tell us? End of century projections 5 C 9 F Hatching indicates IPCC Fifth Assessment Report uncertainty 12

13 Continued warming almost certain for Hawaii Rainfall change uncertain for Hawaii Island topography not resolved in global climate models. 13

14 Rainfall change uncertain for Fiji Future change depends on how much warming. Opposing mechanisms yield uncertainty (in some regions) Wet gets wetter Warmest gets wetter Widlansky et al. 2012, Nature Climate Change 14

15 How will El Niño respond to Climate Change? Climate model projections There is high confidence that ENSO will remain the dominant mode of interannual variability with global influences in the 21 st century, and due to changes in moisture availability ENSO induced rainfall variability on regional scales will intensify. But, There is low confidence in changes in the intensity and spatial pattern of El Niño in a warmer climate. IPCC Fifth Assessment Report El Niño impacts likely to become more extreme even if sea surface temperature variability remains constant Present Stronger anomalies (contours) required to overcome large mean gradients (shading) Future Smaller mean gradients (shading), therefore atmosphere responds to weaker anomalies (contours) Cai et al. 2014, Nature Climate Change 15

16 For example, likely more future extreme sea level seesaws Widlansky et al. 2015, Science Advances Discussion: How should scientists and societies respond to climate change uncertainty? IPCC WG1AR5 Summary for Policy Makers (Table 2) Build high resolution global climate models International supercomputing centres dedicated to climate prediction are needed to reduce uncertainties in global warming, says Tim Palmer. 16