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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 Weather Prediction by Numerical Process Lewis Fry Richardson 1922
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 Nobel Peace Prize to VP Al Gore and UN Intergovt Panel for Climate Change Bert Bolin 5/15/ /30/2007 Founding Chairman of the IPCC [student at 1950 ENIAC calculation]

10 Atmosphere mass energy water vapor momentum convective mixing

11 Ocean momentum mass energy salinity

12 Numerical Weather Prediction ( ~ days)
Initial Conditions t = 0 hr Prediction t = 6 hr 12 18 24 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 Earth’s Energy Balance, with GHG
Sun 30 20 absorbed by atm 100 Earth 70 95 114 23 7 CO2, H2O, GHG 50 absorbed by sfc

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

19 Climate Forcing CO2 change in radiative heating (W/m2) at surface for a given change in trace gas composition or other change external to the climate system CH4 N2O 10,000 years ago

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

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

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

23 Attribution are observed changes consistent with
Observations are observed changes consistent with expected responses to forcings inconsistent with alternative explanations Climate model: All forcing Climate model: Solar+volcanic only Attribution of climate change to causes involves READ Climate models are important tools for attributing and understanding climate change. Understanding observed changes is based on our best understanding of climate physics, as contained in simple to complex climate models. For the 4rth assessment report, we had a new and very comprehensive archive of 20th century simulations available. This has greatly helped. This figure gives an example. You see observed global and annual mean temperature in black over the 20th century compared to that simulated by a wide range of these models. On the top, in red, are individual model simulations and their overall mean shown fat, that are driven by external influences including increases in greenhouse gases, in aerosols, in changes in solar radiation and by volcanic eruptions. The observations rarely leave the range of model simulations. The trends and individual events like cooling in response to volcanic eruptions (POINT) are well reproduced. The fuzzy range gives an idea of uncertainty with variability in the climate system. 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 CO2: 200 years emission, penetrated to upper m Slow warming of oceans --> continue evaporation, continue warming

25 21stC warming depends on rate of CO2 increase
21thC “Business as usual”: CO2 increasing 380 to 680 ppmv 20thC stabilizn: CO2 constant at 380 ppmv for the 21stC Meehl et al. (Science 2005)

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

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

28 Atmosphere mass energy water vapor momentum convective mixing

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

30 Climate Model’s View of the Global C Cycle
FF Biophysics + BGC Atmosphere CO2 = 280 ppmv (560 PgC) + … Ocean Circ. 37400 Pg C 2000 Pg C 90± 60± Turnover Time of C yr time of C 101 yr

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

32 21st C Carbon-Climate Feedback:  = Coupled minus Uncoupled
Warm-wet Warm-dry {dT, 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 2005

33 Changing Carbon Sink Capacity
CO2 Airborne fraction =atm increase / Fossil fuel emission With SRES A2 (fast FF emission): as CO2 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

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 tn Model --> forecast for tn+1 Practice Ensemble forecast --> mean state, uncertainty in forecast Kalnay 2003

36 Approach: Data Assimilation
x=[T, p, u,v, q, s, … model parameters] obs yo tn-1 tn yo xa Find best estimate of x (xan) given imperfect model (xbn) and incomplete obs (yo) xb Model: xbn = M(xan-1) yo

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, H2O, O3, CH4, CO MLS – O3, H2O, CO HIRDLS – T, O3, H2O, CO2, CH4 OMI – O3, aerosol climatology aerosols, polarization CloudSat – 3-D cloud climatology CALIPSO – 3-D aerosol climatology AIRS – T, P, H2O, CO2, CH4 MODIS – cloud, aerosols, albedo OCO - - CO2 O2 A-band ps, clouds, aerosols Coordinated Observations 5/4/2002 4/28/2006 7/15/2004 12/18/2004 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 & sequestrat’n - 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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