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Climate Modeling Inez Fung University of California, Berkeley.

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Presentation on theme: "Climate Modeling Inez Fung University of California, Berkeley."— Presentation transcript:

1 Climate Modeling Inez Fung University of California, Berkeley

2 Weather Prediction by Numerical Process Lewis Fry Richardson 1922

3 Grid over domain Predict pressure, temperature, wind Temperature -->density Pressure Pressure gradient Wind temperature

4 Weather Prediction by Numerical Process Lewis Fry Richardson 1922 Predicted: 145 mb/ 6 hrs Observed: -1.0 mb / 6 hs

5 First Successful Numerical Weather Forecast: March 1950 Grid over US 24 hour, 48 hour forecast 33 days to debug code and do the forecast Led by J. Charney (far left) who figured out the quasi- geostrophic equations

6 ENIAC: <10 words of read/write memory Function tables (read memory)

7 16 operations in each time step Platzman, Bull. Am Meteorol. Soc. 1979

8 Reasons for success in 1950 More & better observations after WWII--> initial conditions + assessment Faster computers (24 hour forecast in 24 hours) Improved physics - –Atm flow is quasi 2-D (Ro<<1) and is baroclinically unstable –quasi-geostrophic vorticity equations –filtered out gravity waves –Initial C: pressure (no need for u,v) – t ~30 minutes (instead of 5-10 minutes)

9 2007 Bert Bolin 5/15/ /30/2007 Founding Chairman of the IPCC … [student at 1950 ENIAC calculation] Nobel Peace Prize to VP Al Gore and UN Intergovt Panel for Climate Change

10 mass energy water vapor momentumAtmosphere convective mixing

11 Ocean momentum mass energy salinity

12 Numerical Weather Prediction ( ~ days) Initial Conditions t = 0 hr Prediction t = 6 hr Predict evolution of state of atmosphere (t) Error grows w time --> limit to weather prediction

13 Seasonal Climate Prediction ( El – Nino Southern Oscillation ) { Initial Conditions} Atm + Ocn t = 0 {Prediction} t = 1 month 2 3 Coupled atmosphere-ocean instability Require obs of initial states of both atm & ocean, esp. Equatorial Pacific {Ensemble} of forecasts Forecast statistics (mean & variance) – probability Now – experimental forecasts (model testing in ~months )

14 Continued Success Since 1950 More & better observations Faster computers Improved physics

15 Modern climate models Forcing: solar irradiance, volanic aerosols, greenhouse gases, … Predict: T, p, wind, clouds, water vapor, soil moisture, ocean current, salinity, sea ice, … Very high spatial resolution: <1 deg lat/lon resolution ~50 atm, ~30 ocn, ~10 soil layers ==> 6.5 million grid boxes Very small time steps (~minutes) Ensemble runs multiple experiments) Model experiments (e.g ) take weeks to months on supercomputers

16 Continued Success Since 1950 More & better observations Faster computers Improved physics

17 Earths Energy Balance, with GHG CO 2, H 2 O, GHG Earth absorbed by sfc Sun absorbed by atm 100

18 Climate Processes Radiative transfer: solar & terrestrial phase transition of water Convective mixing cloud microphysics Evapotranspiratn Movement of heat and water in soils

19 Climate Forcing change in radiative heating (W/m 2 ) at surface for a given change in trace gas composition or other change external to the climate system CO2 CH4 N2O 10,000 years ago

20 Climate Feedbacks Warming Decrease snow cover; Decrease reflectivity of surface Increase absorption of solar energy Increase cloud cover; Decrease absorption of solar energy Evaporation from ocean, Increase water vapor in atm Enhance greenhouse effect

21 J. Zwally Greenland Urgency: Rapid Melting of Glaciers --> accelerate warming Moulin

22 Will cloud cover increase or decrease with warming? [models: decrease; warm air can hold more moisture; +ve feedback] A B + water vapor + longwave abs Warming A C + water vapor + cloud cover + longwave abs - shortwave abs Temperature (K) Saturation Vapor Pressure (mb) A B C liquid vapor

23 Attribution are observed changes consistent with expected responses to forcings inconsistent with alternative explanations Observations Climate model: All forcing Climate model: Solar+volcanic only IPCC AR4 (2007)

24 Oceans: Bottleneck to warming long memory of climate system 4000 meters of water, heated from above Stably stratified Very slow diffusion of chemicals and heat to deep ocean Fossil fuel CO 2 : 200 years emission, penetrated to upper m Slow warming of oceans -- > continue evaporation, continue warming

25 21 st C warming depends on rate of CO 2 increase 20 th C stabilizn: CO 2 constant at 380 ppmv for the 21 st C 21 th C Business as usual: CO 2 increasing 380 to 680 ppmv Meehl et al. (Science 2005)

26 Model predicted change in recurrence of 100 year drought years 2020s 2070s Changes in the probability distribution as well the mean

27 Outlook More & better observations Faster computers climate forcing and surface boundary conditionsImproved physics + Biogeochemistry: include atmospheric chemistry, land and ocean biology to predict climate forcing and surface boundary conditions

28 mass energy water vapor momentumAtmosphere convective mixing

29 Ship Tracks: - more cloud condensation nuclei - smaller drops - more drops - more reflective - energy balance

30 Climate Models View of the Global C Cycle Biophysics + BGC Atmosphere CO 2 = 280 ppmv (560 PgC) + … Ocean Circ. + BGC Pg C 2000 Pg C 90 ± 60± Turnover Time of C yr Turnover time of C 10 1 yr FF

31 Prognostic Carbon Cycle Atm Ocean Land-live Land-dead

32 Warm-wet Warm-dry T, Soil Moisture Index } Regression of NPP vs T Photosynthesis decreases with carbon-climate coupling Fung et al. Evolution of carbon sinks in a changing climate. PNAS st C Carbon-Climate Feedback: = Coupled minus Uncoupled

33 Changing Carbon Sink Capacity With SRES A2 (fast FF emission): as CO 2 increases Capacity of land and ocean to store carbon decreases (slowing of photosyn; reduce soil C turnover time; slower thermocline mixing …) Airborne fraction increases --> more warming Fung et al. Evolution of carbon sinks in a changing climate. PNAS 2005 CO2 Airborne fraction =atm increase / Fossil fuel emission

34 Continued Success Since 1950 More & better observations: –initial conditions, –Analysis --> improve physics –assessment of model results Faster computers Improved physics

35 Initial Condition: Numerical Weather Prediction Challenge Diverse, asynchronous obs of atm Find the current state of the atm at t n Model --> forecast for t n+1 Practice Ensemble forecast --> –mean state, –uncertainty in forecast Kalnay 2003

36 Approach: Data Assimilation y o x=[T, p, u,v, q, s, … model parameters] obs y o t n-1 tntn y o xbxb Model: x b n = M (x a n-1 ) xaxa Find best estimate of x (x a n ) given imperfect model (x b n ) and incomplete obs (y o )

37 Approaches to Merge Data + Model Optimal analysis 3D variational data assimilation 4D var Kalman Filter Ensemble Kalman Filter Local Ensemble Transform Kalman Filter …

38 Observations: The A-Train 1:26 TES – T, P, H 2 O, O 3, CH 4, CO MLS – O 3, H 2 O, CO HIRDLS – T, O 3, H 2 O, CO 2, CH 4 OMI – O 3, aerosol climatology aerosols, polarization CloudSat – 3-D cloud climatology CALIPSO – 3-D aerosol climatology AIRS – T, P, H 2 O, CO 2, CH 4 MODIS – cloud, aerosols, albedo OCO - - CO 2 O 2 A-band p s, clouds, aerosols Coordinated Observations 5/4/2002 4/28/2006 7/15/ /18/2004 Challenge: assimilating ALL data simultaneously in high- resolution climate model to understand interactions

39 Outlook: Research challenges Climate Change Science: High resolution climate projections : Project impact on water availability, ecosystems, agriculture, at a resolution useful to inform policy and strategies for adaptation and carbon management Articulation of uncertainties and risks

40 Outlook: Research challenges Adaptation and Mitigation Production and consumption energy efficiency Alternative energy Carbon capture & sequestratn - scalable? Geo-engineering - potential harm vs benefits Maturity Need a new generation of models where climate interacts with adaptation and mitigation strategies to guide, prioritize policy decisions

41 4th Assessment Report 2007 WGI: Science WGII: Impacts WGIII: Adaptation and Mitigation

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