Christopher Jones Department of Mathematics, University of North Carolina at Chapel Hill And Warwick Mathematics Institute, University of Warwick

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1 Christopher Jones Department of Mathematics, University of North Carolina at Chapel Hill And Warwick Mathematics Institute, University of Warwick Future Directions in Applied Mathematics North Carolina State University, March

2 OBS PHYSICAL LAWS DATA MODEL DA OPTIMAL STATE ESTIMATE OR PREDICTION 2

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4 Gain Matrix 4

5 Where the matrix is determined by the requirement that minimise the cost function: (linearized) observation operator observation error covariance matrix determined by DA scheme being used! 5

6 Possibilities for calculating 6

7 Model + observations prediction model model obs update obs update interpolation interpolation Interpolation at : Sequential DA/Forward Problem/Filtering 7

8 Model run + observations state estimate model model model OBS 4DVAR: Minimise the cost function: Variational DA/Inverse Problem/Smoothing 8

9 Vernieres et al. (2006) Kinneret Conjecture: due to lifting of lower layer of oxygen-free water Occasional fishkill Feeding of 5,000?? 9

10 Top layer momentum Bottom layer momentum u (1),v (1),h (1) ρ (1) =cst u (2),v (2),h (2) ρ (2) =cst 10

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12 Thermistor F K 12

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14 Model alone DA applied Neither model nor data on their own show fishkill, But, together, they do! 14

15 How do we know climate change is happening and accelerating? FACTS Carbon in the atmosphere Human induced PHYSICS Greenhouse effect Longer wavelength of reflected radiation From: IPCC Report WG1, 2007 Joseph Fourier, 1824

16 Warming is NOT uniform

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18 Also we need to understand the Earth system better? Our understanding of the climate of the Earth is akin to that we had of the functioning of the human body in the nineteenth century (Lovelock) Why does mathematics play a key role? many 1

19 Weather prediction: DA is strikingly successful Climate prediction: neither approach is, in an obvious way, useful or even appropriate Conventional wisdom: climate prediction is NOT an initial value problem, but a forcing and boundary value problem 19

20 Paleoclimate: state estimation DA is becoming recognised as an appropriate and useful tool Ocean: IPCC AR5 focus on decadal predictions-need to get ocean initial condition right! DA can be very useful in this endeavour. Key issues in both areas: long timescales and sparse data Workshop: Data Assimilation and Climate Research, June

21 Dalton Minimum (Van der Schrier and Barkmeijer, Climate Dynamics 2005) Cold in Western Europe Decreased solar activity? Volcanoes: 1809 and 1815 Bjerknes Hypothesis: Anomalous oceanatmosphere interaction Southerly flow of cold polar waters into NE Atlantic Ocean Western Europe is cold even into Summer months 21

22 Van der Schrier and Barkmeijer introduce a DA technique to assess relationship between anomalous SLP and cold period 22

23 Atmospheric circulation: Target-used in DA Difference (DJF) between DA and control runs 23

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25 Anomalous wind-stress curl (DJF) SST difference between DA and control (DJF) Conclusions: 1. Anomalous ocean/atmosphere interaction is directly related to Western European cooling, 2. Independent of possible primary forcing due to solar or volcanic activity 3. MOC weakening is present but not significant 25

26 Bayes Gain Matrix 26

27 But: computationally prohibitive, state 27

28 Predictive: State estimation: The minimization gives the posterior mode log posterior of the Lorenz 63 system (courtesy of Jochen Voss) See: Data Assimilation: Mathematical and Statistical Perspectives, Apte, J, Stuart and Voss, IJNMF

29 Primitive eqns of GFD solved on grid Dynamical core of model MUCH of climate science is devoted to setting the forcing terms. This includes: 1. Parameterizing sub-gridscale processes 2. Parameterizing missing effects 3. Information flow to other parts of model 4. Setting ambient conditions (radiative forcing) 5. Tuning the model 29

30 Model runs Model output checked Resetting of model parameters Key signatures, e.g. 1. Does it give correct ice thickness? 2. Do currents lie in correct locations? 3. Is tropical precip right? Accumulated experience Expert judgment OBS DATA 30

31 1. Uncertainty is an intrinsic property of The Model, or a collection of models, and it can be found by just looking at them hard enough Uncertainty of the model can have its origin in many places: data used in tuning, expert judgment etc. Perhaps we need to unpack the model? 2. Data assimilation s primary use is to get the best estimate Data assimilation tells us how to put information together from models and data how the cumulative error is formed. The error may be arbitrarily complex and the issue is how to sample the PDF 31

32 DA could be used in tuning climate models Its use would be similar to the way the paleoclimate work by van der Schrier and Barkmeijer If done from a Bayesian perspective, the procedure would have implications for uncertainty due to model tuning. 32

33 DA is nowhere near ready to be applicable to climate Need to develop DA to deal with o High dimensions o Nonlinearity o Sparse data o Multiple timescales (and space) Shift perspective from getting best estimate of truth to finding PDF that represents all available information 33