Experimental Design of Regional Climate Model experiments PRECIS Workshop Tanzania Meteorological Agency, 29 th June – 3 rd July 2015

Objective of the session To identify and understand the important factors in creating a regional climate model experiment

Why is experimental design important?

Experimentation is part of the Scientific Method

Poor experimental design can lead to unhappiness Wasted Time / Resources Results that can’t be trusted Results that don’t answer the research question False positives / negatives Embarrassment ?

Quality experimental design is worth the effort Resources optimally exploited Experiments complete in an optimum amount of time Results which answer the research question Trustworthy results that pass peer review Successful experiments!

Factors to Consider in Designing PRECIS experiments

PRECIS Graphical User Interface Choice of model domain and resolution RCM, GCM/ Reanalysis and scenario Experiment start date and run length (with spin-up) Output data configurations Run Monitor Stop Map of Region Fine scale configurations to region

The regional domain

Choice of horizontal resolution Two resolution are available: – 0.44  0.44  and 0.22  0.22  – ~50km and ~25km, respectively At 0.22 , improvements will be seen due to better resolved mountains and coastlines For a given area, simulation time at 0.22  degrees is 6 times more than that of 0.44  ~50km resolution

Choice of model domain: Domain Size Large domains will require lots of processing power to complete in a sensible amount of time. This domain is not suitable for a PC, for example.

Choice of model domain: Domain Size Small domains (e.g. the red square over Cape Verde) Are totally dominated by the input lateral boundary data Do not allow internal mesoscale level circulation to develop Will not allow for the regional model to add value

Location of the boundaries Think of water flowing in a stream. If a tree falls into the stream, the uniform flow of the water is changed by the presence of the tree. The tree creates “noise” in the water flow.

Location of the boundaries Mountains have a similar noise- creating effect on atmospheric flow that enters the regional domain as input at the boundaries. So mountains along the lateral boundaries should be avoided whenever possible.

Location of the boundaries Is this a suitable RCM domain? Why or why not?

Location of the boundaries What about this domain over Sri Lanka? Is this a suitable RCM domain? Why or why not?

Choice of model domain: Location of lateral boundaries Domains should Fully encompass the processes of research focus and/or which impact the climate of the area of interest e.g. Include “cyclogenesis” areas in the domain if you are interested in tropical cyclones The area of interest should be in the centre of the domain

Choice of model domain: Location of lateral boundaries Optimum domains: Are not too large or small. 100 grid boxes by 100 grid boxes is a good rule of thumb. Will complete in a reasonable amount of time. Have the area of interest in the domain centre Encompass important climatological influences Have a climate which remains consistent with the driving GCM

The 8 point outer rim The outer rim (8 grid boxes) is where the input coarse data from the GCM is gradually interpolated to the RCM grid. This area must not be analysed. It’s necessary to remove the outer 8 grid boxes altogether.

Choice of model domain: Location of lateral boundary conditions FACTOR TO CONSIDERWhy? a. Ensure that the lateral boundaries are not located over complex terrain (e.g. mountains) Avoids noise due to a mismatch between the regional domain topography and the GCM topography b. When drawing the RCM domain using the PRECIS user interface, ensure that the regional domain for study is inside the grey (inner) box rather than the red/black (outer) box. The outer eight grid boxes are an area of relaxation/interpolation to the RCM grid and are unsuitable for analysis. c. Ensure that the area of interest is far from the lateral boundaries (i.e. that the area of interest is in the interior of the domain) Prevents noise at the lateral boundaries from contaminating the RCM results d. Include forcings and circulations directly affecting the area of interest Allow for development of fine-scale detail in simulated weather and climate variables e. Ensure that RCM results do not diverge from the driving GCM results If RCM results do not agree with the driving GCM, then the climate change results from the model are suspect

Land-surface characteristics

Surface configuration: Land-sea mask editing The land-sea mask is important – The influences of land and sea on climate are very different The default land-sea mask may contain inaccuracies – due to the rotation and regridding of the fractional source data Care is needed for areas of inland water – Surface temperatures are inferred from the nearest ocean areas – Ususally it is advised to change them to land points.

Example: the Aral Sea What’s the problem with this domain with respect to its realism for future climate change studies?

Example: the Aral Sea The PRECIS User Interface in this example is used to mask out part of the Aral Sea (as it exists now) so it is treated like land.

Surface configuration: Topographic height PRECIS allows you to specify the topographic height of individual grid boxes The topographic height must be an average height for the whole grid box It’s almost always not necessary – The default is usually accurate, but consider adjusting the height for very small islands

Surface configuration (MOSES1 only): Land surface characteristics PRECIS allows you to alter the vegetation and soil types for individual land grid boxes Wilson Henderson-Sellers (WHS) dataset has coarse resolution – Small islands in particular are usually incorrectly represented – Utilize local knowledge and datasets Can be used to perform sensitivity studies – e.g. deforestation/desertification

Length of time of the Experiment

Simulation Length Climate varies over seasonal, annual, decadal timescales and beyond A reliable estimate of climate must include an element of decadal variability A good guideline is to simulate at least 31 years of model integration time per experiment. The more years the better!

Example: (El Niño ) Southern Oscillation ENSO has warm and cold phases which have a planetary effect on climate. If I run only a few model years, I don’t give the model the chance to represent both phases

Spin-up time The model takes some time for all of its components to reach full equilibrium The full simulation length should take this into account, as you should not use output data from the spin-up period in any analysis Soil variables at equilibrium Atmospheric variables at equilibrium Up to 1 week 1 year START

Input Data, Output Data and Other Stuff

Driving (Input) Data All of the scenarios are considered equally plausible  simulating climates for a range of scenarios is desirable to assess uncertainty If the running of only one future scenario is possible, choose SRES A1B or RCP 4.5 – Allows intercomparison of results (with, e.g. IPCC AR4, AR5) – Fully transient (spans )

CO 2 in SRES emissions scenarios

RCP Concentrations – CMIP5 (Representative Concentration Pathways (RCPs))

Choice of input GCM data: illustration Projecting the climate into the future means making scientific estimates about levels of greenhouse gases in the future. Using a single estimate isn’t as useful as many estimates. Thomas here can help explain in the way he pushes his cars down the stairs... When he pushes the cars down the stairs, one by one, do they all land/stop in the same place? No!

Make room for a re-analysis Re-analysis GCMs, like all climate models, simulate the atmosphere in time and in three dimensions The difference is that Re- analysis GCMs run in the past and are “constrained” by observations (what actually happened). This is the closest to “4D reality” for the past that we can produce. Limited area models are driven at the boundaries by GCMs or observations.

Choice of Output Data: Variables All of the model data (diagnostic output data) needed must be produced from the experiment PRECIS diagnostics available as standard are a prescribed list of: – Hourly means (optional) – Daily means, maxima, minima (optional) – Monthly, seasonal, decadal means, maxima and minima See the PRECIS manual for a full list of variables and time profiles DR DAVID BECKHAM, CROP MODELLING EXPERT “Crop models generally need rainfall, temperature, sunshine hours (or solar radiation). Sometimes soil moisture or soil temperature, evapotranspiration, and surface pressure. PRECIS provides these as standard, but it’s best to check the crop model before you start the experiment.”

Example: Choice of Output Data: Variables Dr. David Beckham wishes to carry out a study for Singapore on the extreme precipitation produced by a regional model for a present day period and compare it to extreme precipitation produced under a future greenhouse gas emissions scenario. Dr Beckham configures PRECIS to output only monthly and seasonal mean values of total precipitation. Can you see a problem?

Example: Choice of Output Data: Variables Dr. Victoria (“Posh Spice”) Beckham whispers to David that a study of extreme rainfall, which is by definition a rare event, will need DAILY means or even HOURLY mean values of precipitation. With the monthly average, all the intense events will be smoothed/averaged away. David does what Victoria says and configures PRECIS to output daily and hourly mean data. Victoria reminds David that Hourly/Daily configuration produces a LOT of data and that the hard drive can fill up and cause the model to crash.

Number of processors Multiprocessor (MPP) Mode -Possible on shared memory multi-cluster systems (e.g. Quad or dual Quad core) -Runs several times faster than old single core mode -Do not use more cores than your system has or you will slow PRECIS down! -You can run experiments side by side (e.g. Two experiments each using two cores on a quad-core system)

Further Reading PRECIS Handbook: Section 5.1 contains further discussions including references to relevant papers. Past PRECIS users Dawit Abebe (Ethiopia) and Christian Seiler (Bolivia) wrote reports/papers on the process of experimental design they went through to create their domain for PRECIS experiments: – PRECIS_Experimental_Design_Dawit.pdf PRECIS_Experimental_Design_Dawit.pdf – Seiler_Bolivia.pdf Seiler_Bolivia.pdf

Questions?