© Crown copyright Met Office The PRECIS regional climate modelling system and an example of its use David Hein, Met Office Hadley Centre for Climate Change,

© Crown copyright Met Office

The PRECIS regional climate modelling system and an example of its useDavid Hein, Met Office Hadley Centre for Climate Change,

Exeter, UK


Local to regional – this the scale at which much of the climate change related information is most needed

Continental – the scale of much of the reliable information coming from Global Climate Models (GCMs)


Winter precipitation over Great Britain


Higher resolution models are needed in order to simulate tropical storms / hurricanes


What is PRECIS?

• Providing REgional Climates for Impacts Studies

• Is a Regional climate modelling (RCM) system which can be run over any region of the Earth

• Generates detailed climate change projections, and runs on a PC


Why PRECIS came to be

• The majority of climate models run on supercomputers. Supercomputers are expensive to purchase and to run

•Personal computers (PCs) are readily available and have become much more powerful in the past 10 years.


The Components of PRECIS

• PC version of the Hadley Centre’s HadRM3P Regional Climate Model

• resolution 50km (25km for small areas)

• runs on the free Linux operating system

• Easy to use Graphical User Interface to set up RCM experiments

• Data processing and display software

• Boundary conditions (input data)

• Training workshop and materials

• Technical Support

• The PRECIS web site and email address:

• http://precis.metoffice.com

• [email protected]

http://precis.metoffice.com/

mailto:[email protected]


PRECIS: Workshops and Projects

Over 200 trained users from over 60 countries

Extensive regional networks in developing countries (and some developed countries) across the globe

Local affiliated institutions: Cuban Institute of Meteorology (INSMET) Caribbean Community Climate Change Centre (CCCCC, Belize)


Daily weather events from an RCM

Daily precipitation and surface

pressure over New Zealand


PRECIS and extreme precipitation: a case studyFrom “The Representation of Extreme Precipitation in the PRECIS Regional Climate Model, Masters dissertation for David Hein (2008).


Experimental Purpose

• Extreme Precipitation can have a severe impact on human life and livelihood

• Policy makers and planners have an interest in determining how the frequency and intensity of extreme precipitation could change in the coming century

• PRECIS can be used to generate detailed climate projections which will include extreme precipitation

• It is thus important to establish whether PRECIS can realistically simulate detailed extreme precipitation

• “Essentially, all models are wrong, but some are useful.” -- George E. P. Box, Professor Emeritus, University of Wisconsin-Madison


Experimental Set-up

• The PRECIS RCM (HadRM3P) was run over four different areas of the world, each featuring differing characteristics and influences on climate. Output data from the RCM was then compared to historical records of rainfall amount (also called “observations”).

• The model was run between December 1958 and December 1999 over each region

• The input data was from the European Centre for Medium Range Weather Forecasting “ERA-40” quasi-observational data set (i.e. reanalysis data)


Experimental Set-up (Domains)

Europe USA

SouthernAfrica

South Asia


Experimental Set-up

• Precipitation varies in quantity (some days it rains more than other days)

• Precipitation varies in time (it’s not always raining)

• Precipitation varies in space (rain is a localised weather phenomenon)

• Extreme precipitation is, by definition, a relatively rare occurrence

• These factors must be taken into consideration when comparing PRECIS output data against historical observations of precipitation


Indices for Comparison

• Index 1: Multi-annual seasonal mean precipitation

• Provides a “big picture” of how well PRECIS simulates precipitation vs. historical observations (i.e. what actually occurred)

• Seasons are abbreviated via the first day of the month: DJF, MAM, JJA, SON.



• Index 2: Wet day intensity

• Wet day intensity is defined as the multi-annual seasonal mean of precipitation on “rainy days”. A “rainy day” (also called “wet day”) is a day which there is more than 0.1mm of rain.

• Allows for comparison of the total amount of rain which PRECIS produces in comparison to how much rain actually occurred (on “rainy days”)



• Index 3: Wet day frequency

• Wet day frequency is percentage of the total days in which precipitation occurs

• Allows for comparison between how often PRECIS produces precipitation vs. how often precipitation occurred in historical records



• Index 4: Extreme Precipitation

• Allows for comparison of the times when PRECIS produces rainfall in the upper 5% vs. the upper 5% of historical observations.

• Useful to gauge how well that PRECIS simulates extreme precipitation



• Index 5: Pooled Extreme Precipitation

• Because Extreme Precipitation is a rare occurrence, the sample size is often insufficiently large to obtain statistically significant results

• Spatial pooling considers values from neighbouring grid boxes as sampling from the same precipitation population due to being close together. This increases the sample size and the “signal” of extreme precipitation can be boosted.


Further information

• The Bias is the difference between the area-averaged values of the PRECIS output data and the observations.

• Pattern Correlation is a value which quantifies how well that PRECIS matches the observations spatially (i.e. is PRECIS producing rain in the same areas that the observations show)


Further information

• Descriptive terms are used to give a clear assessment of how well the model is doing:


Europe

• For DJF/JJA/SON seasonal means, the model produces values which are Good in comparison to observations in quantity (Bias) and upper Fair to Good spatial correlation (Patt Corr).

• MAM shows Fair bias and spatial correlation - PRECIS produces 32% more precipitation than observations


Europe

• For JJA seasonal mean, PRECIS (left) simulates both the amount and spatial distribution well in comparison to observations (right).


Europe

• For MAM seasonal mean, PRECIS (left) produces too much precipitation over mountainous areas in comparison to observations (right). This is a common problem for models.


Europe

• For Wet Day Intensity, PRECIS is not producing enough rainfall on “wet days” in comparison with observations, and spatially matches Fair to Poor.

• For Wet Day Frequency, PRECIS is producing too many days when precipitation occurs, although it captures the spatial distribution well.


Europe

• PRECIS produces rainfall too often in comparison with observations …


Europe

… But not enough when it does rain. There are too many light rainfall days.


Europe (extremes)

• PRECIS simulates Extreme Precipitation for DJF and SON very well (in amount and location)

• JJA shows a very low model bias, but Poor pattern correlation, indicating that PRECIS is producing very close to the amount of extreme rainfall for this season, but not in the correct locations

• Spatial pooling boosts pattern correlation at the expense (in some cases) of an increase in model bias


Southern Africa

• The overall picture for Southern Africa is that PRECIS produces too much precipitation (high biases)

• PRECIS does better when comparing wet day intensity, but pattern correlation is Poor.

• For wet day frequency, pattern correlation is Good, but again PRECIS is producing rain about twice as often as occurred in the observations


Southern Africa• This plot shows annual mean area averaged precipitation for

PRECIS and the observations. PRECIS gets the trends correct, but is producing roughly twice as much precipitation.


Southern Africa (extremes)

• In general, pattern correlations are better than for the other indices (WDI, WDF, etc), implying that PRECIS is doing better at simulating the upper tail of the distribution

• Pattern correlation for MAM and SON is Poor, although the biases are Fair or Good. For these seasons, PRECIS is performing better in the amount of extreme precip being produced, but not where it occurs.


Southern Africa (extremes)

• DJF pooled extreme precipitation shows PRECIS reproducing the amount of spatial distribution (the east-west difference in extreme precipitation)


Continental USA

• Overall, PRECIS performs well with Good or high Fair values for both bias and correlation.

• PRECIS is slightly too wet in DJF and MAM and slightly too dry in JJA and SON.


Continental USA

• PRECIS produces too much precipitation in the mountainous west, but overall does well in capturing the spatial patterns of wet day intensity


Continental USA

• PRECIS performs very well in capturing the spatial distribution of wet day frequency for JJA. Example: the observations show it rains almost everyday in Florida, and rarely in California. PRECIS reproduces this feature.


Continental USA

• Annual mean precipitation produced by PRECIS matches the trends of the observations very well


Continental USA (extremes)

• Pattern correllation for pooled Extreme precipitation is excellent, as well as biases for JJA and SON. In these seasons, PRECIS simulates Extreme Precipitation well, both in amount and spatially


Continental USA

• For DJF pooled extreme index, the pattern correlation is extremely good -- PRECIS is producing extreme precipitation in all the right places. However, it produces too much extreme precipitation in the southeast and mountain west


Conclusions

• Overall, PRECIS showed better performance in time and location in the regions in which large-scale (i.e. frontal) precipitation dominates (the USA and Europe) than in the regions in which convective rainfall dominates (South Africa and India)

• Mountainous areas were problematic for PRECIS in that it tended to overestimate rainfall in these areas

• Important to note that a single index of model performance (e.g. multi-annual seasonal mean) can hide many problems and is therefore no sufficient for a thorough model validation.


End

© Crown copyright Met Office The PRECIS regional climate modelling system and an example of its use David Hein, Met Office Hadley Centre for Climate Change,

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