© crown copyright met office providing high-resolution regional climates for vulnerability...

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Providing High-Resolution Regional Climates for Vulnerability Assessment and Adaptation Planning

Joseph Intsiful, African Adaptation Programme

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• Climate scenario

“A scenario is a coherent, internally consistent and plausible description of a possible future state of the world. It is not a forecast; rather, each scenario is one alternative image of how the future can unfold.”

Key point 1: Internal consistency

Socio-Economic scenario → Emissions scenario → Climate scenario

Key point 2: Scenarios are NOT the same as ‘predictions’: we can have many plausible scenarios.

Climate scenarios

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Types of climate scenarios

• Incremental scenarios for sensitivity studies

• Analogue scenarios

• Scenarios based on outputs from Climate Models

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• Particular climatic elements are changed incrementally by plausible though arbitrary amounts.

• Use for testing system sensitivity

• Use for identifying critical thresholds or discontinuities in climate

• Potentially leads to unrealistic scenarios

• Not related to anthropogenic emissions

1. Incremental scenarios

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• Identify recorded climate regimes which may resemble the future climate in a given region.

• Spatial analogues

• Temporal analogues

• Palaeoclimatic

• Instrumental

• Not related to anthropogenic emissions

• Often physically implausible

2. Analogue scenarios

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• Coupled Atmosphere-Ocean Global Climate Models (AOGCMs)

• Coarse resolution, and often have large biases

• Based on physics

• Internally consistent

• Dynamically downscaled AOGCMs

• High resolution GCMS (e.g. PRECIS)

• Require large computer resources

• Can inherit biases from AOGCM

• Statistically downscaled AOGCMs

• Statistical methods are based on current climate and trained on short-term variability

• Difficult to develop internally consistent climate variables

3. Scenarios based on outputs from Climate Models

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Predicting impacts

IPCC

National research centres

You!

CORDEX

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… from a global climate model (GCM) grid to the point of interest.

Downscaling

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300km Global Model

25km Regional Model

50km Regional Model

Observed 10km

Winter precipitation over Britain

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Regional climate models (RCMs) simulate high resolution weather

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RCMs simulate extreme events e.g. tropical cyclones

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Represent smaller islands

Projected changes in summer surface air temperature between present day and the end of the 21st century.

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What is a Regional Climate Model?

• Mathematical model of the atmosphere and land surface (and sometimes the ocean)

• ‘High’ resolution: Produces data in

grid cells < 50km in size

• Spans a limited area (region) of the globe

• Contains representations of many of the important physical processes within the climate system

• Cloud

• Radiation

• Rainfall

• Atmospheric aerosols

• Soil hydrology

• etc.

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Boundary conditions

• Limited area regional models require meteorological information at their edges (lateral boundaries)

• These data provide the interface between the regional model’s domain and the rest of the world

• The climate of a region is always strongly influenced by the global situation

• These data are necessarily provided by global general circulation models (GCMs)

• or from observed datasets with global coverage

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More adverse than beneficial impacts on ecological and socioeconomic systems

are projected

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• Evaluation of the detrimental and beneficial consequences of climate change on natural and human systems.

• Impacts models require climate scenarios as inputs.

• The impact of the climate change is determined by contrasting the effect of the observed/baseline climate with that of the future climate (scenario) on the exposure unit

Impacts Assessment

Using climate model scenarios with impact models

• To assess an impact a baseline needs to be established, i.e. the impacts model needs to be run with baseline climate information

• This baseline climate information can either be taken from a climate model simulation of present-day conditions or directly from observations

• If the baseline climate information is taken from a model simulation of current climate then the future climate scenario can be derived directly from a future climate simulation

• If the baseline climate is taken from observations then climate change scenario information from a climate projection needs to be combined with this baseline to provide the future climate scenario

• The impacts model is then run either of these future climate scenarios and the results compared with the impact baseline

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One approach to combining climate observations and simulations

• If, as a result of systematic biases in the GCM/RCM simulations, the impact baseline is unrealistic then a simple approach is to apply the model change factor rather than the model output directly

• Model change factor = Model future – Model baseline: or

• Model change factor =(model future / model baseline) *100

• We can then add the change factor to an observed record to get a future scenario with the bias seen in the baseline removed

• Future climate scenario = Observed + model change factor: or

• Future climate scenario = Observed * model change factor (%)

• This approach may provide impact results which are more reasonable but the simple change factor applied does not account for changes in variability and may result in inconsistent future climates

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Example: Modelling Impacts of climate change on agriculture

Use of PRECIS and the crop model CERES to simulate yield changes per hectare of three grain crops (rice, wheat, maize) in China when applying one future climate scenario and a representation of CO2 fertilization

Xiong et al, 2007, Climate change and critical thresholds in China’s food security, Climatic Change, 81:205-221

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Example: modelling climate change impacts on Hydrology

• Change in water stress in the Ganges-Brahmaputra-Meghna Basin derived using the Global Water Availability Assessment (GWAVA) model

(CLASIC project – work with CEGIS)

2020s

2050s

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Example: Modelling Storm Surge under climate scenarios

Simulated tropical cyclone and resulting storm surge. Produced using PRECIS and POLCOM storm surge model

SLR projections from GCM

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An impacts case-study

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Climate change scenarios from a recent climate model: estimating change in runoff in southern Africa

• Nigel Arnell

• (Dept. of Geography, University of Southampton, U.K.)

• Debbie Hudson and Richard Jones

• (Hadley Centre for Climate Prediction and Research)

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Methods

• Runoff: calculated from water balance

• runoff = precipitation – evaporation – absorption by soil

• Two sets of models - a climate model and a runoff model

• Baseline climate → Run-off model → Baseline Run-off

• Future climate → Run-off model → Future Run-off

• Compares different methods of constructing future climate scenario

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Mean temperature and rainfall

Average annual rainfall is systematically over-estimated by the model

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Rainfall variability is accurately represented by the model

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Constructing the baseline and future timeseries of data required by the runoff model. For example:

…these are just some of the possibilities

Some different methods for CCS construction

BASELINE FUTURE CLIMATE SCENARIO

Mean Variance Mean Variance

Observed Observed Observed + model

difference

Observed

Simulated Simulated Simulated Simulated

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The best method CCS construction in this case?

BASELINE FUTURE CLIMATE SCENARIO

Mean Variance Mean Variance

Observed Observed Observed + model

difference

Observed

Simulated Simulated Simulated Simulated

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Summary

• There are several techniques for producing future climate information

• Only climate model based climate change predictions can be used for providing climate scenarios which are plausible and self consistent

• Even when using a single climate model (or family of models) there are many different ways to provide climate change information for impacts studies

• The method of climate scenario construction adds a further uncertainty in assessing impacts of climate change

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Questions and answers