Part 2 Generations of Projections IPCC Model Projections over time - - PowerPoint PPT Presentation

Case Study 4

Part 2

Morning Session

Generations of Projections

IPCC Model Projections over time…

(all models, all experiments) + 2.6°C + 1.9°C

0.5 1 1.5 2 2.5 3

SAR TAR AR4 AR5

Sudbury Annual Temperature Change for the 2050s

(from 1981-2010 baseline)

1995 (11) 2001 (14) 2007 (171) 2013 (208)

(all models, all experiments) + 6.3 % + 4.5 %

IPCC Model Projections over time…

1 2 3 4 5 6 7

SAR TAR AR4 AR5

Sudbury Annual Precipitation Change (%) for the 2050s

(from 1981-2010 baseline)

1995 (11) 2001 (14) 2007 (171) 2013 (208)

Model CO2 sensitivity – the link between GHG and Change

Its been relatively steady – we double CO2 and the models

• ver time generate similar

temperature change Have we been wrong for 100 years? Source: Maslin and Austin, 2012

Why are there even differences between Models?

• Spatial Resolution – as we have seen
• Different sensitivities to GHG forcing (not all are as ‘reactive’ to a given RCP)
• Assumption of land surface type
• Assumption of elevation
• Boundary conditions, initial conditions
• Varying degrees of complexity in oceanic and atmospheric physics and their

connection (always a tradeoff between complexity and computational requirements)

• The BIGGIE – how each model deals with sub-grid (or within a single grid)

processes like SNOW, ICE, SOIL LAYERS (number, character), CLOUD

Model Ensembles And Uncertainty

Ensemble considerations for practitioners

• AVAILABILITY of the data / processing demands!
• The use of a limited number of models or scenarios provides no

information of the uncertainty involved in climate modelling – ensembles can help

• Although each GCM represents the ‘best effort’ of each

modelling centre, there are biases

• The use of an ensemble (mean/median) of models tends to

converge to a ‘best estimate’ by reduction of strong biases in single models

• There are other alternatives to ensembles as well which will be

demonstrated – depends on the stakeholder demands

The IPCC is very clear that the use of a limited number of models is not recommended for decision-making (Guidance Document)

Research studies support ensembles

Most of the current published literature considers the multi-model approach

• IPCC-TGICA, 2007: General Guidelines on the Use of Scenario Data for Climate

Impact and Adaptation Assessment. Version 2. Prepared by T.R. Carter on behalf of the Intergovernmental Panel on Climate Change, Task Group on Data and Scenario Support for Impact and Climate Assessment, 66pp.

• Gleckler, P. J, K. E. Taylor, and C. Doutriaux (2008) Performance metrics for climate
• models. Journal of Geophysical Research. Vol. 113. D06104.
• IPCC Expert Meeting on Assessing and Combining Multi Model Climate Projections,

Boulder, Colorado, USA, 25-27 January 2010

http://www.ipcc.ch/pdf/supporting-material/IPCC_EM_MME_GoodPracticeGuidancePaper.pdf

Discussion: Should we ‘weight’ the models? Or treat them all equally? Good and bad?

• Researchers have shown that using this approach (averaging may estimates) best fits

historical climate – over the use of any single model

• The use of a single model ‘puts all our eggs in one basket’ – we are assuming one model

is correct

• The use of multiple models allows us to obtain some indication (but a PROXY only), of

model uncertainty (whether the model estimates are all ‘close’ (greater confidence) or ‘spread out’ (less confidence)

• A proven track record has long been used in weather forecast models (forecasters would

look at all models to inform their final decision)

• Although each modelling centre is a best effort – there are biases
• So there is an assumption of a ‘convergence’ on a best estimate using ensembles by

removing strong single model biases (cancellation)

Why the Emphasis on Ensembles?

The Ensemble Estimate – value of ensemble

Source: Nat’l Geo – Brain Games

https://www.youtube.com/watch?v=j__w-7s8GPo

• The ensemble method provides us a direct measure of MODEL agreement, not directly climate

projection uncertainty, although these we would suspect are related

• The Standard Deviation of model results over each gridcell can give us an INDICATION or

CHARACTERIZATION of model certainty/uncertainty Areas of low SD = Areas of higher model agreement Areas of high SD = Areas of lower model agreement

• But what if everyone is wrong?
• A question of PRECISION (models agree) vs ACCURACY (models correctly define climate)
• We know models are not ideal – they must PARAMETERIZE real life, they simplify some processes

Why is the Ensemble not a direct measure of uncertainty?

Poll Two…

Precision versus Accuracy

Source: NOAA

A B C D

POLL QUESTION 2

Where do YOU think we are with Climate Models?

How Good is the Ensemble Anyway?

• We can test this out using historical gridded observed datasets at similar resolution (like NCEP)
• Acknowledge the models are developed/calibrated against these datasets so not completely independent!

NCEP (National Centers for Environmental Prediction) U.S. AR5 Ensemble (All models/all model runs) MEAN ANNUAL TEMPERATURE (1981-2010) Good Agreement

How Good is the Ensemble Anyway?

NCEP (National Centers for Environmental Prediction) U.S. AR5 Ensemble (All models/all model runs) MEAN ANNUAL PRECIPITATION (1981-2010) Precipitation is in average mm/day

NOT as successful as Temperature – but this is commonly found

Models vs Observations – box plots from RSI Analytics

1981-2010 Mean Temperature – Observed and the AR5 Ensemble Bioclimate Location: Geraldton, ON Good Fit

Models vs Observations – box plots from RSI Analytics

1981-2010 Precipitation – Observed and the AR5 Ensemble Bioclimate Location: Geraldton, ON Not as Good Fit in some months

• Statistical downscaling can be useful when we have good specific observational data

to relate to model projections

• Requires customized input datasets and generally for only a small subset of models
• Includes ‘weather typing’ technique – relationship between weather events (ice

storm) and large scale (e.g. if this situation is found it is ASSOCIATED with these types of events)

• Both general techniques (Dynamical and Statistical) can be complementary, but IPCC

relies on models (Dynamic)

What about GCM vs RCM projections (Dynamical) vs Statistical Downscaling?

Global versus Regional Models in Ontario

• Look at 3 projections (using RCP8.5, 2050s difference from 1981-2010 annual temperature):

RCM HIGH RES output RCM HIGH RES output GCM output CanRCM4 (25 km) contoured CanRCM4 (50 km) contoured CanESM2 (~200 km) contoured

Comparison: GCM vs RCM T Deltas for Ontario

+5.4 +5.4 +3.5 +3.5 +5.6 +3.6

A Test – What if we apply GCM to hi-res baseline?

CanRCM4 Mean T at 2050s Bias Removed CanGRD with GCM Delta T for 2050s

And if we remove the CanRCM4 Bias – we get a close match

A Test – What if we apply GCM to hi-res baseline?

• The take away?
• This method is an efficient way to incorporate both BIAS correction (we use REAL
• bserved baseline) AND we have many GCM projections available
• The previous example just uses the Canadian GCM – but any of the AR5 models

could be the ‘delta’

• The projection was different because the CanRCM4 model is too warm even

historically - so we get a ‘too warm’ 2050s temperature

• This could very likely be improved IF we apply the GCM ensemble instead of a

single model <this is the typical RSI methodology>

Using the Data - Best Options

Using the Data - Best Options

• Look primarily at the RCP4.5 (low) and RCP8.5 (business as usual) emission projections
• If only one option – the most likely would be RCP8.5
• Apply all the models you can!
• Look at the model CHANGES from the baseline (Deltas), since the individual model is likely biased
• If you do consider a single model, can it be characterized at least within the entire model collection?
• Best NOT to combine emission scenarios – but consider them as separate possibilities (high and low)
• Monthly output is generally available – daily is often not and requires large storage/computations
• If daily is required (hydrological model?) – it may be a good approximation to apply monthly deltas to
• bserved daily data to generate proxy future data

Characterizing Uncertainty

Using the Data – Characterizing Uncertainty

RCP4.5 Ensemble RCP8.5 Ensemble

• Obtain the data
• Regrid the models to a common

resolution

• Use all model runs for each

RCP 4.5 RCP8.5 N 95 84 Mean Delta +2.5 +3.7

• Std. Dev.

0.68 0.80 5th Perctile 1.5 2.4 95th Perctile 3.8 5.1 Max Value 4.4 5.4 Min Value 1.1 2.3

4 6 8 10 12 14 16 18

Temperature (°C)

Toronto Regional Average – Mean Annual T

RCP4.5 RCP8.5

Using the Data – Characterizing Uncertainty

• So we must add observed

StD to the ensemble model projection StD

• If we are using JUST ONE

MODEL (no ensemble) then the model ideally includes the observed StD

Observed St Dev Range Observed St Dev Range PLUS the ensemble Under Normal Distribution: +/- 1 StD = 68% prob, +/- 2 StD = 95% prob

Using the Data – Characterizing Uncertainty

2080s Projections – ALL RCPs 2050s Projections – ALL RCPs 2020s Projections – ALL RCPs

Not unexpectedly, ensemble StD increases going forward in time – just like weather forecasts

Temperature Change (C)

Confidence In Climate Change Model Results

Extreme Variables and Their Difficulty

Extremes:

• Extremes are likely to always be underestimated by models… (grid averaging) vs

point

• CC Models don’t contain the physics associated with extreme events, but are

improving

• Higher resolution requires more precise defs of land surface underneath – this

becomes increasing important since different surfaces have their own microclimates – when we move to <1km res – important --- how do we know this in the future?

• Models don’t have varying surface types going forward! Assume static land

surface, no ‘urbanization’

Extremes – A Challenge for Models – Especially GCMs

• 2 m Air Temperature Range (C)
• Consecutive Dry Days (days)
• Days with Rain > 10 mm/d (days)
• Fraction of Annual Total Precip > 95th percentile (%)
• Fraction of Time < 90th percentile min temp (%)
• Number of Frost Days (days)
• Maximum Heat Wave Duration (days)
• Maximum 5 Day Precipitation (mm)
• Simple Daily Precipitation Intensity Index (mm/day)
• Growing Season Length (days)

Basic Model Output vs Value-added and Extremes

In the last IPCC Assessment many modelling centres also calculated ‘Extreme Variables’

Most In-demand Variables: Precipitation Related

• Drainage and permafrost thaw issues, flooding
• Impact depends on whether from spring rainfall on snowmelt, thunderstorms,

weather systems…

• New codes, standards, infrastructure designs, approaches
• e.g. CSA Community Drainage Standard, CSA Rainfall Intensity-Duration-

Frequency Guide

• Affects so many sectors!
• But precipitation and extremes are a challenge

Precipitation Extremes - Trends

Extreme Precipitation Indicator Name Future Trends (greater than MEAN!)

Maximum 1 Day Precipitation UP Maximum 5 Day Precipitation UP Daily Precip Intensity Index (Precipitation Amount/Number Days) UP Extreme Precipitation (Occurrence of 95th & 99th Percentiles) UP Consecutive Dry Days

UP Theory: 1. A warmer atmosphere can hold more moisture (the Clausius–Clapeyron), so there is more moisture available for intense rainfall events under future conditions

• 2. A more vigorous water cycle/evaporation increase/convection

Precipitation Extremes – Future Trends

Example: Toronto – AR5 GCM Ensemble – RCP8.5 – SIMILAR for Sudbury

5 10 15 20 25 30 35 40 45

Percentage Change

Toronto Area - 95th Percentile Precipitation Change -Annual

5 10 15 20 25 30 35 40 45

Percentage Change

Toronto Area - Mean Precipitation Change

• Annual

EXTREMES CHANGE MORE THAN MEAN

• Many applications require this
• EC releases the official curves using standard
• Provides information of rainfall intensities DOWN TO 5 MINUTE INTENSITIES
• Greatest intensities are meteorologically related to CONVECTION – a process which

is very localized, (small spatial and temporal scales) And that is the problem! Model output and IDF demands are at odds

This continues to be an active area of research and different studies can produce very different results Once again – we must rely on the consideration of multiple estimations/techniques Will also consider IDF in afternoon case studies since so important for many sectors

Specialty Product: Intensity-Duration-Frequency Information

Sources of Data – Global and Ontario

Sources of Climate Information

Environment Canada Historical data (station, IDF, normals, trends): http://climate.weather.gc.ca OMECC Climate Change Adaptation Tool Box and the Ontario Regional Adaptation Collaborative (ORAC) info gatewaylink (York U, U Regina, U Toronto, Guelph, OURANOS, etc): http://tinyurl.com/ClimateOntario OCCIAR has a Data Page with Links to Historical and Projection Data: http://www.climateontario.ca/CData.php For customized/specialized – undoubtedly requires experienced specialists There is no standard or certification for the provision of this information ‘the wild west out there’

Ontario Specific: CCDP (U Regina)

Ontario Specific: LAMPS (York U)

Ontario Specific: MNR

Climate Change Assessment Approach

1. Describe current climate conditions 2. Define climate conditions associated to risk (thresholds) 3. Calculate existing climate-related risk (probabilities) 4. Characterize potential future climate 5. Assess potential future climate-related risk 6. Prioritize and develop management responses

Our Approach: Climate change assessment steps broadly defined

We apply this approach through, for example, the PIEVC process of Engineers Canada

Poll Three…

POLL QUESTION 3

Which one of the following conditions has the least projection confidence?

• a. Warmer winters
• b. Tornadoes and hurricanes
• c. Permafrost coverage is likely to decrease
• d. Daily precipitation extremes are likely to increase
• e. Atmospheric moisture will likely increase

Climate Change Summary

Climate Change Summary

• Climate change is already happening, showing impacts
• Our best adaptation measures require the best science
• Ensemble projections are current best-practices and represent due diligence,

consideration of uncertainty

• New IPCC projections are consistent with historical trends
• We appear to be on the ‘high’ projection pathway based upon our greenhouse

gas emissions

• Climate change will have significant impacts on mean climate but models

suggest it will impact extremes even more

The Reality:

• Although models are our best means of considering climate change, they are

not perfect – they approximate reality

• This is an ongoing process – models are always being improved, but there are

computational and scientific limitations

• Some variables are much better captured than others
• The use of many estimates together (ensembles) is much preferred over a

single estimate

• Typically a trade-off between many coarse resolution models vs few high

resolution models

The Reality:

• Higher resolution doesn’t necessary mean better accuracy
• All models have biases (errors in even duplicating historical climate)

– varying approaches to correct for this – easiest: ‘Delta Method’ – look at CHANGE only

• Apply change to baseline – generate future climate, assumption of

similar distribution – Observed StD is not the same as model ensemble StD

Future of Climate Projections

The Future:

• AR5 assessed over 30,000 papers, over 1000 authors,
• ver 1000 reviewers
• Climate change is one of the most researched topics
• IPCC pledges to involve more developing countries

(Africa/Asia priority)

• Make the info relevant, not only to policymakers – but

society

• Continue assessment reports every 5 to 7 years
• Make reports more ‘user-friendly’

The Future:

• Province and now FEDS have indicated Climate Change is a priority
• MOECC going through consultation process now, carbon fees since similar

policies exist elsewhere, green energy (opportunity!)

• Ontario Environment Commissioner – Investigation of data portals? How to

proceed? Always better communication methods

• Hopefully greater national/provincial coordination with new FED gov’t
• Requirements for future infrastructure builds to consider Climate Change
• Codes and Standards will increasingly include Climate Change factors

Communication: Local Climate Analogs?

“What locations now have a temperature Thunder Bay and Sudbury are projected to have in the 2050s?”

Minneapolis Toronto

So perhaps we look to these locations now to see future impacts and adaptation responses ‘Weather Tales’ – what events happen there now? Is this our future? Real issues…

The Future:

• Increasing spatial and temporal resolution of

models

• Increasing complexity – less simplification of

processes/ more realistic

• Unfortunate circumstances:

The uncertainty will at least partially decrease due to an increasing climate change signal vs natural variability Long residence time of GHGs will promote the higher RCP possibilities with the greatest consequences

Source: Myhre et al. 2015. Nature.

• Afternoon Sessions will take this background info and consider how we

apply this to TWO specific sectors

Coming up after lunch break…