EST 5101 Climate Change Science Chapter 5. Climate Models. Rezaul Karim Environmental Science & Technology Jessore University of science & Technology

Transcription

1 EST 5101 Climate Change Science Chapter 5. Climate Models Rezaul Karim Environmental Science & Technology Jessore University of science & Technology

2 Chapter outline overview of climate modelling; How Are Models Constructed; Simulating Climatic Change; Advances in Modelling; How reliable are current climate model predictions?

3 Reference IPCC, 2001: Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change ( Chapter 1 & 5)

4 Outline Introduction Why do we need models? Basic processes Radiation Atmospheric/Oceanic circulation Model basics Resolution Parameterizations Limitations

5 Why do we need models? Weather forecast What will the weather be in Norrköping tomorrow? What activities shall we plan for the weekend?

6 Why do we need models? Climate analysis How large is the natural variability? Mechanisms of climate processes?

7 Why do we need models? Climate scenarios How will the climate be in Norrköping in 30 years? Do we will have more extremes? How is sea level changing? Simulated temperature change until

8 The Climate System : What do we need to include?

9 Radiation: Black body radiation A black body absorbs all incident radiation A blackbody emits radiation according to Planck s law (shape of curves) Wien s displacement law give the temperature of a radiation source (maximum of curves) Total flux given by the Stefan-Boltzmann law (integration over the curve) wavelength [µm]

10 Longwave radiation of the Earth Emitted radiation at the Earth s surface 4-100µm (maximum at around 10µm) CO 2 and O 3 absorb at wavelengths within the Earth s emission spectrum. Increases in their concentration will increase the natural greenhouse effect and warm the planet. Water Vapour is the most active abosrbing gas in the IR spectrum.

11 Solar radiation Absorption of incoming solar radiation small Incoming radiation may be reflected by clouds, particles or by the ground The albedo (A) is the ratio between reflected and incoming radiation Cloud albedo varies (30-90%) Global average ca 30% (including clouds) Properties of the ground Snow Old snow Ice Sand Grass Forest Water Water (Sun close to horizon) Albedo (%)

12 Radiation balance of the Earth Assume balance between outgoing and incoming radiation on long term basis Solar constant 1368 W m -2 planetary albedo 30% Outgoing terrestrial radiation (longwave) is absorbed and reemitted in the atmosphere. The net effect is a warming of the surface (Te = 288 K)

13 Radiation Balance, Differential Heating Imbalances leads to temperature differences and thereby pressure gradients generating the general circulation of the atmosphere (and the oceans) Long term imbalance leads to climate change

14 Atmospheric motion Air is under influence of a number of forces resulting in movements (winds and turbulence) The forces are; the pressure gradient force, gravity, friction, centrifugal forces and the Coriolis force, The Coriolis force is an apparent force that leads to a deflection to the right (left) of all motion in the northern (southern) hemisphere It is proportional to the speed and depends on latitude (increasing towards the poles)

15 Atmospheric Circulation No rotation of the Earth cooling heating cooing Conservation of absolute angular momemtum and the stability of fluid flows leads to the break up of a thermally direct circulation around 30 poleward of the equator. Here the atmosphere develops instabilities (extra-tropical cyclones) that efficiently transport energy and momentum poleward.

16 Large scale ocean circulation The ocean circulation is driven by density contrasts in the ocean. Regions of intense heat loss from the ocean, surface winds and salinity of the ocean (sea ice melt, runoff, precipitation) govern the circulation. The continents play an important role.

17 Equations describing the atmosphere u t r u + V u + ω p fv + φ = x v r v φ + V v + ω + fu + = t p y φ = α p T r T + V T + ω αω / Cp = t p F X F y Q / Cp r V q t + p α = RT r + V ω = 0 p q q + ω p The atmosphere is governed by a set of physical laws expressing how the air moves, heating and cooling, moisture, and so on. Although the equations describing atmospheric behaviour can be formulated, they cannot be solved analytically. Instead, numerical methods are needed to provide approximate solutions. = S q

18 A global climate model (model describing the general circulation - GCM) In a GCM grid boxes cover the entire planet (ocean and atmosphere) Typical size is km in the horizontal Iayers in the vertical both in atmosphere and ocean Typical time step can be 30 min

19 Climate Model The information needed to run a GCM (atmosphere and ocean) is: Initial state of all the variables in all boxes A description of the land surface (topography and land use) Solar radiation Gas and aerosol composition of the atmosphere The resources needed to run a GCM (atmosphere and ocean) are: Super computer (many processors & 100 TB disk) Takes 2 weeks for 100 years simulation

20 Space and time scales Typical timescales of variation in the climate system. Atmosphere (seconds to weeks) Surface vegetation (weeks to years) Surface snow and sea-ice (days to years) Upper Ocean (days to years) Deep Ocean (months to multi-century) Glaciers (years to multi-century) Continental distribution and mountain building (100s to 1000s of thousand years)

21 Parametrized processes in a climate model Sea ice processes Mixing Deep Convection Eddies

22 The Development of Climate models Mid-1970s Mid-1980s Early 1990s Late 1990s Early 2000s Late 2000s Source: IPCC, TAR, 2001

23 Types of Climate Models There are many different types of climate models Huge range of complexities, physical processes represented, etc E.g., compare a seasonal forecast model to an ice age cycle model Today we ll focus primarily on the climate models for global warming predictions (from IPCC AR4/AR5) Data is publicly available from PCMDI website

24 General Circulation Models (GCMs) What are the components of these models? What are the essential physical processes that are being modeled? How have the models of these physical processes evolved over the history of climate modeling?

25 Climate Models We ll discuss with two examples: The discovery of chaos by Ed Lorenz The first climate models of Suki Manabe But first: Climate models are closely related to weather prediction models Let s discuss some history of weather prediction using computer models And the first attempt at numerical weather forecasting by Lewis Fry Richardson

26 Numerical Weather Prediction (NWP) Improvements in weather prediction over the last 60 years are among the most impressive accomplishments of society Northern Hem. 3 day forecast Southern Hem. 3 day forecast 5 day forecasts (NH & SH) 7 day forecasts (NH & SH) Simmons & Hollingsworth 02

27 Lewis Fry Richardson British mathematician, physicist, atmospheric scientist Scientific career very influenced by his Quaker beliefs (pacifism) Made the first numerical weather prediction in 1922 Also had a dream of the future of weather prediction

28 Richardson s Dream: The Forecast Factory Filled with employees ( computers ) doing calculations Richardson s dream in 1922 of a global forecasting system He estimated 64,000 computers (people) would be necessary to forecast over the globe Much info from the next few slides is from a book by Peter Lynch (U Coll Dublin)

29 The First Successful NWP Experiment John von Neumann, Jule Charney, Ragnar Fjortoft (1950) Research proposal proposed three uses for NWP: Weather prediction (duh) Planning where to take observations Weather modification!

30 Weather Forecasting vs Climate Forecasting Weather models and climate models are similar in a lot of ways Use very similar mathematical equations But weather forecasting and climate forecasting have very different goals How can we predict the climate in 50 years if we can t predict the weather 2 weeks from now?

31 Chaos Ed Lorenz was running a computer model & put in slightly different inputs He found the predictions were similar for a while but then wildly diverged to different solutions Chaos: when small changes make a big & unpredictable difference Edward Lorenz! ( )! meteorologist, M.I.T.! father of chaos theory!

32 Climate Forecasts This limit to weather prediction doesn t affect climate forecasts It all averages out after a few months of storms Climate forecasts: Summer is hotter than winter After a strong volcano blows up, the Earth will cool The Earth will be hotter with more greenhouse gases Shifts in weather patterns when El Niño is present Etc

33 Suki Manabe: Father of Climate Modeling Syukuro Manabe (born 1931): Worked at GFDL from : Director of Earth Simulator, Japan

34 Early Manabe Modeling Studies Radiative model: M. and Moller (1961) Radiative-convective model: M. and Strickler (1964) Atmosphere only model: Smagorinsky, M. and Holloway (1965)

35 AGCM Components AGCM: Atmospheric General Circulation Model Dynamics : Fluid equations on a rotating sphere Physics : Radiative transfer Surface fluxes/boundary layer scheme Clouds Moist convection

36 Dynamical Core of AGCMs Essentially just fluid equations on the rotating sphere

37 More Dynamical Core Details Momentum equations: Coriolis terms: due to rotation of Earth (not sphericity) Metric terms : to account for sphericity Energy equation: Energy balance is in the standard fluid equations Goes into the GCM without approximation

38 Numerical Methods Gridpoint methods: Fields specified at points Common resolutions: 2x2.5 deg (90x144 points) Spectral methods: Uses Fourier representations of fields around latitude circles Common resolutions: T42 (64x128; 2.8 deg), T85 (128x256; 1.4 deg) Highest resolution model in AR4: T106 (1.1 deg resolution)

39 Model Resolution Evolution Changes in resolution over time: AR = assessment report FAR = first AR, etc FAR: 1990 SAR: 1995 TAR: 2001 AR4: 2007 AR5: 2014

40 Physics of AGCMs Climate models have some very complex parameterizations of physical processes Radiative transfer Convection Clouds Surface fluxes/boundary layer schemes We ll describe general ideas of how these are parameterized And the history of some of the parameterizations

41 Radiative transfer models Clear sky radiative transfer is essentially a solved problem Divide electromagnetic spectrum into bands Solar absorption and scattering by H2O, CO2, O3, O2, clouds, aerosols

42 Radiative transfer models Longwave absorption and emission by H2O, CO2, O3, N2O, CH4, CFC-11, CFC-12, CFC-113, HCFC-22, aerosols, clouds 8 longwave bands Very computationally expensive! Often a large percentage of the total CPU usage is running the radiation code In some models, not called every time step

43 Moist convection schemes Convection: vertical overturning due to density differences Atmosphere is strongly heated from below, leading to large amounts of convection Moisture complicates convection significantly (huge heat source)

44 Moist convection schemes Classical goals of cumulus parameterization (Cu param): Precipitation Vertical distribution of heating and drying/moistening Non-classical goals of Cu param: Mass fluxes (for movement of pollution, etc) Generation of liquid and ice phases of water Interactions with PBL, radiation, and flow (momentum transport) Goals from review by Arakawa (2004)

45 Cloud schemes Cloud interactions are the most uncertain process in GCMs Lead to the largest differences between models

46 Cloud schemes Historical implementations of cloud parameterizations: First, climatological cloud distributions were used (e.g., Holloway and Manabe 1971) After that, diagnostic cloud parameterizations were used Based on properties such as relative humidity, vertical velocity, and static stability E.g., Wetherald and Manabe 1988: clouds when relative humidity exceeds 99% Slingo 1987: Diagnostic scheme based on convective precipitation, humidity, vertical velocity, and stability

47 Surface Flux Parameterization Surface flux schemes How much evaporation & heat flux comes off the ocean/land SH = C v (T Ts) Surface drag coefficient C is a function of stability and shear Monin-Obukhov similarity theory Neutral drag coefficient: just a function of surface roughness & von Karman coefficient Surface roughness values for different surfaces

48 Boundary Layer Parameterizations Boundary layer scheme How heat, moisture and momentum are distributed in the turbulent boundary layer Typically based on turbulent closures with empirical data Matched to Monin-Obukhov surface layer Some have an additional prognostic variable, the turbulent kinetic energy Gives memory to the mixing

49 Additional GCM Parameterizations Shallow convection UW shallow convection scheme is implemented in GFDL s AM3 model (for AR5) UW scheme is a single-plume mass flux scheme Other ways: Diffusive schemes Adjustment Cumulus momentum transport Gravity wave drag Momentum fluxes due to gravity waves near topography

50 Climate Modeling Centers Modeling centers from CMIP3 Very international effort! GFDL, Princeton, NJ NCAR, Boulder, CO

51 Other Successful Predictions of Climate Models More warming at night than day Most warming in Arctic than anywhere else (especially during winter) Least warming in/around Antarctica Wet regions get wetter, subtropical regions dry Tropopause moves upward Large scale tropical circulations weaken