Introduction to Climate Models: History & Basic Structure Topics covered: 1. Climate models 2. Brief history 3. Parameterization 4. Uncertainties 5. Palaeo applications

Models are of central importance in many scientific contexts: the Bohr model of the atom, the MIT bag model of the nucleon, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka-Volterra model of predator-prey interaction, the double helix model of DNA, agent-based and evolutionary models in the social sciences, In science we are are spend a great deal of time building, testing, comparing and revising models. Models are one of the primary instruments of modern science. Models in science

Definition and a healthy philosophy regarding climate modelling

The Climate System

Brief History of Climate Modelling 1922: Lewis Fry Richardson basic equations and methodology of numerical weather prediction 1950: Charney, Fjørtoft and von Neumann (1950) first numerical weather forecast (barotropic vorticity equation model) 1956: Norman Phillips first general circulation experiment (two-layer, quasi-geostrophic hemispheric model) 1963: Smagorinsky, Manabe and collaborators at GFDL, USA 9 level primitive equation model 1960s and 1970s: Other groups and their offshoots began work University of California Los Angeles (UCLA), National Center for Atmospheric Research (NCAR, Boulder, Colorado) and UK Meteorological Office 1990s: Atmospheric Model Intercomparison Project (AMIP) Results from about 30 atmospheric models from around the world 2007: IPCC Forth Assessment Report climate projections to 2100 from numerous coupled ocean-atmosphere-cryosphere models.

Types of Model Energy Balance Models Earth Models of Intermediate Complexity General Circulation Models Goose et al. (2010) – Online Textbook

General Circulation Model’s (GCM) include: Also can include: carbon cycle, aerosols, chemistry, biology, ice sheets, etc. AtmosphereOcean Sea iceVegetation

ATMOSPHERE LAND OCEANICESULPHUR CARBONCHEMISTRY ATMOSPHERE LAND OCEANICESULPHUR CARBON ATMOSPHERE LAND OCEANICESULPHUR ATMOSPHERE LAND OCEANICE ATMOSPHERE LAND OCEAN ATMOSPHERE LAND ATMOSPHERE Development of Met Office Models Component models are constructed off-line and coupled in to the climate model when sufficiently developed 1960s Now 1988

General Circulation Modelling (GCM) Initial conditions, provided only at time=0 Temperature, Pressure, Humidity, Clouds, Winds etc. Boundary conditions, provided throughout simulation can include: Trace gases (e.g. CO 2 ) Continental configuration Solar forcing Ice sheets

Fundamental Equations

Supercomputers

The Hadley Centre GCM – HadCM3 HadCM3 can produce >50 years of model simulation per day Newer versions of the Hadley Centre Model are much slower ~2 years/day.

Parameterized processes in the ECMWF model

The horizontal and vertical resolutions of climate models need to be high enough to avoid numerical errors and to resolve the basic dynamical and transport processes. There is a trade-off between resolution and computing time, but model resolutions are increasing continually, as more computer power becomes available. There is also a trade off between model complexity and computer time. Balancing Competing Demands

Simulation of the historical or near-historical record Simulation of the historical or near-historical record Analysis of the observed record of variability Analysis of the observed record of variability Projection for the next 100 years Projection for the next 100 years Primary Research Focus in Climate Change Science Greatest Strengths Spatial and temporal character of the Observations. Measurement of physical quantities that define the state of the atmosphere and ocean. Greatest Weaknesses Sense of change. Sense of the integration of the Earth System.

Greatest Strengths Spectacular sense of change (Furry Alligator Syndrome) True integrated system response Greatest Weaknesses Proxies rather than state variables Limited spatial and temporal resolution In contrast: A Research Focus in Earth History “The greatest weaknesses in a research focus on the modern record are the greatest strengths of Earth System History”

Do models agree? IPCC, AR5, Fig TS.14

Uncertainties Source: IPCC Fourth Assessment Report Regional patterns of change from different models are different

Models have less agreement for rainfall than temperature projections IPCC, AR5, Fig TS.16 MMM signal is not significant (within internal variability) Signal is significant and models agree on sign of change

Uncertainties Source: IPCC Fourth Assessment Report Models differ in their projection of dangerous events

Why do uncertainties exist? Models have “errors” i.e. when simulating present-day climate and climate change, there is a mismatch between the model and the observations. Differences in model formulation can lead to differences in climate change feedbacks.

Uncertainty: The lack of certainty, A state of having limited knowledge where it is impossible to exactly describe the existing state, a future outcome, or more than one possible outcome. Uncertainty

How to deal with uncertainties? Continue to improve models until global and regional projections converge Climate change already happened by the time models converge Is convergence a useful indicator of reliability? Use techniques other than comprehensive climate modelling Cannot extrapolate from noisy (possibly non-existent) time series Simple models cannot provide all the information Climate change may not be linear Combine information from climate models, observations (+ palaeo) and understanding to quantify the uncertainty in projections Risk-based approached used in other disciplines where scientific uncertainties exist

Climate Change Projection/Retrodiction In the presence of uncertainties in climate model projections we can adopt a probabilistic approach Sources of uncertainty: Initial conditions, natural variability Boundary conditions, emissions/concentrations of greenhouse gases and other forcing agents Model errors and uncertainty, different models giving different projections Probabilistic climate projections (for e.g. 2100) cannot be easily verified in the way that probabilistic weather forecasts are. Challenges in the world of palaeo data/model comparisons too.

Climate Change Projection/Retrodiction …however, we can still use ensemble and probabilistic techniques in climate change projection/retrodiction Need a different strategy for generating the ensemble as initial conditions are not the leading source of uncertainty The “multi-model” ensemble, MME The “perturbed-physics” ensemble, PPE Need something in place of the verification cycle – assume that models which are good at reproducing observed/palaeo climate change are also good at simulating future climate change.

Multi-Model Ensemble A collection of the world’s climate models, sometimes called an “ensemble of opportunity”. Currently coordinated by projects like CMIP-Coupled Model Intercomparison Project and housed at PCMDI, California, PMIP (LSCE, France). A relatively large “gene-pool” of possible models, although it is common to share some components Models are “tuned” to reproduce observed data – although formal tuning is not performed

Perturbed Physics Ensemble Take one model structure and perturb uncertain parameters and possible switch in/out different subroutines. Can control experimental design, systematically explore and isolate uncertainties from different components Potential for many more ensemble members Unable to fully explore “structural” uncertainties HadCM3 widely used (MOHC and climateprediction.net) but other modelling groups are dipping their toes in the water

Comparison of MMEs and PPEs Global mean temperature change in MMEs and PPEs under different scenarios PPEs capable of sampling global response uncertainties

We make an assumption that a model which is good for the modern is good for the past (or future): how good are our model parameters? Examples from Palaeoclimatology

A good model for the modern may not be a good model for the past (or the future) We need to perform out of sample evaluations of model performance and therefore we must work on palaeo and we must have a strong connection to the data community Pre-industrial summer sea ice concentration %Pliocene summer sea ice concentration % Pliocene sea-ice

Constraining models with data

Pliocene Uncertainty… Mean annual SST comparison (with model and data uncertainty) Degree of data-model discordance (anomaly versus anomaly) Challenges…

UncertaintySpace Causes of data/model mismatch

DATAMODEL At each site temperature or precipitation change is a function of the prevailing orbital, and other forcings, that change over time. Unless proxy syntheses truly reconstruct geographically extensive single events in time they will suffer from aliasing Temperature and precipitation predicted for a fixed moment in time with a single prescribed set of forcings and a prescribed set of boundary conditions (CO 2, ice sheets etc.) Hypothesise that a significant component of data-model inconsistencies that we have documented in deep time is because we are not comparing like for like with regard to data and models. In deep time we have compared apples and oranges

A lesson from and practical application of Milanković Theory

Displays a near modern orbital configuration, before and after the time slice In a known warm peak in the benthic oxygen isotope record New interval to focus on (KM3/5 to M2)

Pliocene Uncertainty… New model results showing the differences in annual mean SAT between two interglacial events during the Pliocene (Prescott et al. 2014). Seeing through the mists of time

Pliocene Uncertainty… New model results showing the differences between two interglacial events during the Pliocene (Prescott et al. 2014) Seasonal differences in SAT are more prominent - greater implications for environmental reconstructions. Seeing through the mists

Demonstrated problems with aliasing in proxy records. Necessitates move forward towards Quaternary methods of modelling and data collection. Targeting narrower windows of time and discrete time slices. [Prescott et al. 2014] Peak warmth is not synchronous

Different modelling approach

Exploring the unknown…together

Can Models Reproduce Climates of the Past? No! Sometimes To within known uncertainties often they can! Yes or no isn't really the point, ask a better question

PalaeocenePresent Antarctica: from Greenhouse to Ice-house Mechanisms for Antarctic Ice-Sheet Initiation Ocean Gateways Ocean Gateways Antarctic elevation Antarctic elevation Declining atmospheric CO 2 concentration Declining atmospheric CO 2 concentration

DeConto & Pollard (2003). Antarctic ice sheet growth (E/O)

Gateways and circulation

Understanding the ocean circulation response (Eocene- Oligocene)

Cretaceous Climates & Ice-Sheets Palaeobotanical Evidence Cretaceous forest 120 million years ago on the Antarctic Peninsula. reconstruction based on PhD of Jodie Howe, University of Leeds/BAS, painted by Robert Nichols.

Evidence for large, rapid sea-level changes (Miller et al., 2005) Evidence for eustatic nature Pace and magnitude suggest glacial origin. Suggest moderate-sized ice sheets ( × 10 6 km 3 ). Paced by Milankovitch forcing. CO 2 levels through the Maastrichtian 2 greenhouse episodes ppm But suggestions of CO 2 low-points at ~70 and 66 Ma. (Nordt et al., 2003; Beerling et al., 2002).

How to create a Maastrictian model Change solar output~0.6% less than present CO 2 (and other gases)4 x pre-industrial (but could be 2x to 8x. Volcanic activityAssume same as today. Change in orbitSame as present, but perform sensitivity simulations PalaeogeographyIncluding sea-level/ orography/ bathymetry/land ice Previous modelling also required prescription of vegetation, and sea surface temperatures (or ocean heat transport) but this is no longer needed.

Maastrictian Orography At climate model resolution. Original palaeogeographies from Paul Markwick

2xCO 2 - ~ 2 x 10 6 km 3 ice Suite of HadCM3 derived palaeoclimates 2, 4, and 6 x CO 2 Further runs being carried out including 1 x CO 2 Comparison against climate proxy database Climate then used to drive a BAS ice-sheet model. 2xCO 2 4xCO 2 + favourable orbit Ice Sheets in a Greenhouse World

Model developments – proxy formation models 16 18

Isotope diagnostics available H 2 16 O and H 2 18 O available in: Ocean water Precipitation (rain and snow) Clouds Atmospheric vapour Soil moisture Canopy moisture runoff Fluxes between reservoirs Other useful diagnostics: temperature precipitation amount winds (circulation patterns) vegetation cover + many more

 18 O in precipitation from HadCM3 Tindall et al. 2009

Scientific Output Using HadCM3  18 O Early Eocene (~55ma) SST T°C=a-b(  18 O c -    sw ) Tindall et al EPSL 2010

Summary General Circulation Models are useful tools to understand climate Whilst originally developed to improve our understanding of the system, they are now commonly used for climate prediction However complex they still represent a simplified version of reality Quantifying uncertainty is now a major scientific activity Earth history can provide out of sample tests for climate models Caution is needed in interpreting data/model discord Model developments will improve the links between data and models Models and data can be used to address big hypotheses proposed to understand the evolution of planets climate and environments.