uncertainties in climate modeling and climate change projections precis workshop tanzania...

Download Uncertainties in climate modeling and climate change projections PRECIS Workshop Tanzania Meteorological Agency, 29 th June – 3 rd July 2015

Post on 21-Jan-2016




0 download

Embed Size (px)


Title of Presentation

Uncertainties in climate modeling and climate change projectionsPRECIS Workshop

Tanzania Meteorological Agency, 29th June 3rd July 2015Stress that this is a substantial topic and the science / approaches are continuously evolving. In this session we will only cover some of the main issues.1Aims of this sessionExamine fundamental concepts of climate uncertaintiesUnderstand the cascade of uncertaintiesProvide detail on main sources of global climate change uncertaintiesProvide detail on uncertainties in regional climate change predictions2Here, the aims are to explore uncertainties in climate modeling in a more general sense. The talk on ensembles will take a more practical approach to how we deal with uncertainties in climate modeling.What is uncertainty?

Uncertainty = lack of certainty Crown copyright Met OfficeAsk the participants what their definition of uncertainty is? If there is a white board, write up some of the key words (probably along the lines of error, probability, chance, ignorance, unknowns etc) A number of different answers will be given and state that all are valid (probably) as uncertainty means different things to different people. However it is useful to remember that uncertainty simply means lack of certainty every prediction of the future (be it economic, political, about the planets etc) is uncertain. Yet climate scientists tend to have a very specific framework for thinking about uncertainties in climate prediction and how to quantify them.

The cartoon shows that having more than one clock makes you uncertain about what the time actually is.

3Uncertainties in climate predictionWilby and Dessai, 2010 Robust Adaptation to Climate Change

choices Crown copyright Met OfficeDiscuss the different components in the cascade of uncertainty. Some of them are inherent and irreducible, others are the subject of intense scientific endeavours to try and reduce the uncertainties but no matter what, we must account for a range of futures.

The box draws attention to the focus of this workshop. Explain that the ensembles talk will go into more detail on the choices that can be made i.e. How to sample the uncertainty but that this still forms only a partial component of the overall cascade of uncertainties that ultimately decision makers must consider.4

First, an analogy... Crown copyright Met Office

Crown copyright Met OfficeThis analogy links to the sources of uncertainty that form the framework for the rest of the talk, and indeed a framework applied widely in climate science.

Hold a dice in the air. Ask the participants to predict what number it will land on dropping it to the ground. Hopefully they will say that they dont know. Then ask them what they would need to know in order to predict exactly. Encourage them to think about different aspects e.g. The height above the ground, whether it is a normal six sided unweighted dice, what the exact starting position is, what the surface of the floor is etc.

The analogy is that for a perfect prediction, we need to know perfectly the initial position, the full extent of the system (e.g. It has six sides) and the forcings on/in the system (is it weighted, is there a wind blowing). Also, we assume the world is Newtonian! This is a hypothetical example in which we could perfectly predict the outcome. But this analagous to a weather forecast.

If we are interested in the climate, we want to know the likelihood of any number over a number of throws i.e. Does it land on a two 20% of the time. Then explain that in practice we could find this out by rolling it a large number of times.

However, we cannot do this for the real climate system we have to rely on imperfect models (show another dice) the question is, can we use the model to say something about the real system.

Emphasise that this is just an analogy and that hopefully it is useful but it is obviously not the full story.

6Sources of climate projection uncertaintyScenario uncertainty Human and natural emissions of greenhouse gasesTranslating emissions into concentrations of greenhouse gases and their effect on system radiative forcings

Initial Condition (IC) uncertainty Sparse/incomplete observations in time and spaceErroneous and uncertain observations

Model uncertaintyModel error and inadequacyParameter uncertainty

Crown copyright Met Office7

What is climate uncertainty? Crown copyright Met Office

CertaintyIf we have:A perfect model ANDPerfect initial observationsthen, and only then, can we make statements of certainty.

e.g. Probability of getting a 1 = 0Probability of getting a 2 = 0Probability of getting a 3 = 1Probability of getting a 4 = 0Probability of getting a 5 = 0Probability of getting a 6 = 0UncertaintyIf we have:A perfect model ANDImperfect initial observationsthen the best we can do is make probabilistic statements.

e.g. Probability of getting a 1 = 1/6Probability of getting a 2 = 1/6Probability of getting a 3 = 1/6Probability of getting a 4 = 1/6Probability of getting a 5 = 1/6Probability of getting a 6 = 1/6

Crown copyright Met OfficeThis extends the analogy earlier but provides some numbers:

1. A perfect model (where the model and the system are the same) with perfect understanding and initial state could lead to an entirely accurate deterministic forecast (ignore relativity for now)

2. If we dont have perfect initial observations, perhaps the best we can do is state the climatology i.e. 1 in 6 chance9Deeper uncertaintyIf we have:An imperfect model ANDImperfect initial observationsthen we can saynothing?

e.g. Probability of getting a 1 = ?Probability of getting a 2 = ?Probability of getting a 3 = ?Probability of getting a 4 = ?Probability of getting a 5 = ?Probability of getting a 6 = ?The best we can do is make conditional probability statements. Probabilities are conditional on the model used in the analysis. The crucial question is:

How good is our model at representing reality?

In weather prediction we can verify forecasts and update our models based on this information. In climate prediction, we dont have this luxury. We have to rely on other sources of confidence, such as:

model agreement ability to represent driving (synoptic) processes appropriate propagation and representation of teleconnections ability to capture past climate behaviour Crown copyright Met OfficeWith an imperfect model, we will not get the same climatology as the real system. How do we know if the numbers we get out of our model are informative of the real system?

...explain the different sources of confidence in climate models/prediction.10Initial ConditionPredictabilityBoundary ConditionPredictability

From Hewitson et al 2013Predictability Crown copyright Met OfficeNow we move back to the real climate system put the dice away.

This hypothetical schematic describes that the skill of forecasts changes with prediction lead time (i.e. How far into the future we predict), and with aggregation over time (i.e. Seasonal average versus 10 year average). There is high skill at short time horizons and it decreases over time but it can increase as we aggregate statistics over longer time frames say that we can be very confident the 2080s will be warmer than the last decade, but less confident that the 2020s will be warmer. Also, it suggests that for certain time scales, the level of information needed for decision making may be lower than for other time scales. At some time scales, we may never be able to provide the necessary information to guide specific decisions owing to theoretical limits to predictability.


Uncertainty in predicting the weatherImperfect initial observationsPerfect model scenario

We can make probabilistic statements.The probability of the temperature being at least 14 degrees is 0.7

P(T=11) = 0.0P(T=12) = 0.1 P(T=13) = 0.2P(T=14) = 0.5P(T=15) = 0.2P(T=16) = 0.0ProbabilityTemperatureIn reality, we have imperfect models but we can verify our forecasts with observations of the system.

By identifying biases and sources of model error, we can update our models and improve predictions.The next two slides show how uncertainty for weather forecast differs compared to uncertainty for climate forecasts.

Here, we may be predicting the temperature for a location say Exeter at some lead time. The best we can do, even with a perfect model, if we have imperfect initial observations is a probabilistic forecast. With an imperfect model, there will be errors introduced the aim in weather forecasting is to reduce these errors so the probabilities are as accurate as possible.12We have inherent irreducible uncertainty, so even armed with a perfect model, we have to consider probabilities. In the imperfect model, the probability distribution is conditional on the model assumptions.

Climate is what you expect

ProbabilityTemperature10th percentile90th percentile future changeprobabilityweatherforecastclimatepossible future climateNatural internal variabilityModel uncertainty+90th percentile changeprobabilityFuture climate is what you expect to expect

Climate uncertainty...is different Crown copyright Met OfficeWe cant make a weather forecast accurately beyond 10 days to 2 weeks. Thus we move to climate...

For climate, we talking about a distribution with a mean, percentiles etc. In the future, the climate distribution may change and we care about what the changes are to statistics in the distribution.

Because we have internal variability in the climate system, one 30 year period will be different from the next 30 years, irrespective of any changes in external forcings (e.g. Solar forcing). The


View more >