Downscaling / Regionalization Techniques and methodologies AIACC Workshop, Apr 2002 Bruce Hewitson CSAG : University of Cape Town

Downscaling: Techniques and methodologies General concepts and assumptions Regional Climate Models : Overview of application Empirical-Statistical downscaling : Overview and application issues Decision approach to downscaling choices

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Simple Complex ReliableDangerous Downscaling: a valuable procedure of tremendous potential facing a minefield of choices ? ? ? ? Do you need to downscale? What do you NEED rather than WANT For the scientific question you are asking, can you do with a simple sensitivity study, use the native GCM data, apply interpolation, add the GCM anomaly to a baseline data set, or has some else already generated a suitable product??? ?

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Downscaling: A technique to take GCM atmospheric fields and derive climate information at a spatial/temporal scale finer than that of the GCM Note: the downscaled predictand can only contain variance that exists in the cross scale relationship captured by f. Anything else is/must be “made up” “Local” Climate = f (larger scale atmospheric forcing) R = f (L) R: predictand - (a set of) regional scale variables L: predictors - large scale variables from GCM f : stochastic or quantitative transfer function conditioned by L, or a dynamical regional climate model.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Two options premised on the same assumptions Regional Climate Models (RCMs) or Empirical cross scale functions Assumptions: The GCM is skillful (enough) with regard to the predictors used in the downscaling -- are they “adequately” simulated by the GCM “Adequate” requires evaluating the GCM in terms of the predictor variables at the space and time scales of use! e.g: For RCMs this could mean the full 3-dimensional fields of motion, temperature, and humidity, on a 6-12 hour time interval, over the domain of interest. Note: Downscaling propagates the GCM error

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Two options premised on the same assumptions Regional Climate Models or Empirical cross scale functions Assumptions: f is valid under altered climatic conditions - stationarity Note: This applies to empirical downscaling and and RCMs. If the climate system is substantially non-stationary, then at the very least empirical downscaling becomes questionable, possibly much of RCM applications as well. ie: the bulk of future synoptic states are at least represented in present day records -- the future dominated by changes in frequency, intensity, and persistence.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Two options premised on the same assumptions Regional Climate Models or Empirical cross scale functions Assumptions: The chosen predictors represent / contain the climate change signal. For example (empirical downscaling): if local temperature is well determined by synoptic scale sea level pressure (SLP), which shows minimal change into the future. An effective empirical downscaling may be derived, but, what if atmospheric moisture content goes up? The downscaled  T from SLP may be ~0, yet a large  T may actually exist from the moisture change.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Regional Climate Models Computationally intensive, physically based (in part), likely the most viable/valid downscaling in the long term, still somewhat developmental. Conceptual approach: Scale an AGCM to a finite domain, calibrate paramterizations for higher resolution, couple a land surface scheme, force at the boundaries with atmospheric fields from the GCM - simple?

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Regional Climate Models Computationally intensive, physically based (in part), likely the most viable/valid downscaling in the long term, still somewhat developmental. Conceptual approach: Scale an AGCM to a finite domain, calibrate paramterizations for higher resolution, couple a land surface scheme, force at the boundaries with atmospheric fields from the GCM - simple? Conceptual issues: How to interface at boundariesBoundary field updating Inflow versus outflowParameterization schemes Land surface schemeNumber of levels Spatial resolutionDomain sensitivity GCM versus RCM physics 1 -way versus 2-way nesting

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Regional Climate Models Practicalities: Complex procedure with many implementation decisions that can determine the result obtained. Need to understand why you get the results you see (right answer for wrong reason problem). Selection of domain, physics package, parameterization, and evaluation of performance is a time-consuming procedure, but essential! Running a RCM, given suitable IT skills and resources, can be done in a matter of days. Achieving understandable and justifiable results can be very lengthy.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Case example from Africa of implementing an RCMs Instituting an RCM in an environment where it has not been run before 16 scientists from around Africa, two week training workshop, full IT support, theory lectures, all software and scripts configured, follow up with participants. 18 months later, 7 active participants, not all of whom achieved successful simulations at their home institution.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Running a RCM -- A brief exposure to typical activities Select a model: preferably one that will run on available computational infrastructure, with an established user base, and make a (friendly) contact with an experienced user. Develop appropriate skills: Unix literate, Fortran/C capable, data handling and visualization skills. Implement appropriate infrastructure: Single PC can handle months to 1 year type simulations. Longer climate simulations require PC clusters or multiple-CPU workstations.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Running a RCM -- A brief exposure to typical activities Select a model: preferably one that will run on available computational infrastructure, with an established user base, and make a (friendly) contact with an experienced user. Develop appropriate skills: Unix literate, Fortran/C capable, data handling and visualization skills. Implement appropriate infrastructure: Single PC can handle months to 1 year type simulations. Longer climate simulations require PC clusters or multiple-CPU workstations. MM5 v3, land surface model, 110x100 grid points, 23 levels, 60km resolution Simulation setupResults

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Running a RCM -- A brief exposure to typical activities Domain and resolution: If no one else has done it, establish domain sensitivity for region of interest. Select horizontal resolution, vertical levels, physics options. Undertake appropriate sensitivity studies. Prepare boundary conditions: Establish a means of ingesting boundary field data into the RCM (and getting it out). Develop reference climatology: Undertake a 10+ year simulation with reanalysis boundary conditions.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Running a RCM -- A brief exposure to typical activities Domain and resolution: If no one else has done it, establish domain sensitivity for region of interest. Select horizontal resolution, vertical levels, physics options. Undertake appropriate sensitivity studies. Prepare boundary conditions: Establish a means of ingesting boundary field data into the RCM (and getting it out). Develop reference climatology: Undertake a 10+ year simulation with reanalysis boundary conditions. Evaluate reference climatology: This is critical …. if the RCM is not appropriately simulating key processes, generating a future climate anomaly pattern has little meaning. Note: “point and click” solutions are coming (and very welcome), BUT be wary of running an RCM over a new region without careful evaluation.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Running a RCM -- A brief exposure to typical activities Apply GCM control simulation fields, run 10+ year nested simulation. This provides the reference climatology to which the future climate simulation will be compared. Evaluate the climatology, does the GCM/RCM combination generate an appropriate regional climate. Apply GCM future climate simulation, run 10+ year simulation: Finally, the regionalized future climate! “Signal to noise”: Ideally, repeat control and future climate nested simulations with other ensemble members from the GCM runs. Then repeat with another GCM! Analyze your future climate, and the climate anomaly: Can you understand and explain why the future climate anomaly it the way it is. Use the regionalized climate data: Either as direct results, or possibly by adding the regional anomaly to your baseline climatology.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Regional feedbacks RCMs are powerful in allowing investigation of process response to feedbacks and forcings other than from GHG. Example for southern Africa: Vegetation is almost certain to change from climate change forcing. What is the feedback to the atmosphere, and the consequent exacerbation or mitigation of climate change? NPP for 20% increase and decrease on the record of T, RH, and ppt simulated by SDGVM.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Change in plant functional types modelled by the SDGVM for a 20% increase in precipitation. The cross-hatching shows areas of change

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Regional feedbacks Experiment design: MM5v3 RCM, domain over sub-equatorial Africa, albedo perturbed by 20% (within range of natural variability). 3 ensemble simulations for summer with and without perturbation. Results: indicate mean temperature change by up to 0.75 degrees. Response is from a of change in the dynamics of circulation, moisture transport, and cloud formation. Future climates may perturb albedo by far more than 20%.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Empirical/Statistical downscaling A plethora of competing and diverse algorithms of widely different strengths and weaknesses

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Significant lack of systematic evaluation …. “More co-ordinated efforts are thus necessary to evaluate the different methodologies, inter-compare methods and models” IPCC, TAR 2001 Advantages: Computational efficiency Rapid application to multiple GCMs Tailoring to target variables (eg: storm surge) Applicability to broad range of temporal and spatial resolutions Accessibility beyond the modeling community Complementary to regional modeling

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Two extremes to categories of downscaling: Transfer functions relating atmospheric forcing to target variable Stochastic functions and pure weather generators For both: variance explained as a function of the large scale flow, residual variance can only be stochastically generated. For future climate, only change due to the signal contained in the GCM scale forcing can be accounted for ….. Variance explained Synoptic scale Local scale Synoptic Forcing Sub-grid scale forcings

AIACC Workshop, Apr 2002 CSAG : University of Cape Town For climate change: … what proportion of response will be due to sub-GCM grid scale structure -- independent of the large scale forcing? …how stationary is the downscaling function -- applicable to both transfer functions and stochastic functions. Q: for a given location, which is dominant: local or synoptic forcing?

AIACC Workshop, Apr 2002 CSAG : University of Cape Town High-res SST 1° SST Zonal SST Example of synoptic dominance: (From a RCM experiment) Experiment: Precipitation as a function of three different SST fields with identical NCEP boundary forcing. Precipitation (primarily convective) is temporally consistent independent of the SST fields. Implies dominance by synoptic state. ie: The variance at sub-GCM grid cells is still conditioned by large scale flow, empirical downscaling of future change is strongly viable.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town General categories of methodologies Transfer WeatherStochastic conditionedStochastic FunctionTypingon weather type Typical downscaling modes: Downscale from atmospheric instantaneous state to the local climate response (eg: daily precipitation) Downscale secondary variable (eg: stream flow) Relate atmospheric indices (eg: SOI, NAO) to climate statistics Time downscaling -- downscale the diurunal cycle

AIACC Workshop, Apr 2002 CSAG : University of Cape Town General categories of methodologies Transfer WeatherStochastic conditionedStochastic FunctionTypingon weather type Derives a quantitative relationship between predictor(s) and predictand(s) eg: Station daily temperature = f (Sea Level Pressure & 500hPa gpm) f typically a regression style function, can / should be non-linear. Requires training data of adequate duration to span the range of events found in future climate. If predictands are patterns (eg: EOF) or indices (eg: NAO), one assumes stationarity of the pattern or index into the future. Derived function used with GCM field to downscale control and future climate. Can apply to any target scale, including stations.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town General categories of methodologies Transfer WeatherStochastic conditionedStochastic FunctionTypingon weather type - Method under-predicts peaks, over predicts minimum -- characteristic of a generalization function - Residuals represent variance not captured, either from inadequate predictors, or due to local forcing not represented in GCM fields Example: ANN-based downscaling of daily rainfall Effective at capturing temporal evolution consistent with atmosphere. Capture low frequency variability well (seasonal and interannual) Residuals (missing variance), can easily be added stochastically.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town % variance of residuals is proportional to information in predictors or Skill of f is proportional to information in predictors Downscaled station precipitation from 1° MRF assimilation data Possible role in downscaling nested models to point resolution?

AIACC Workshop, Apr 2002 CSAG : University of Cape Town General categories of methodologies Transfer WeatherStochastic conditionedStochastic FunctionTypingon weather type Derives a quantized relationship between predictor(s) and predictand(s) eg: Station temperature = f (type of Sea Level Pressure pattern) Comes from the “synoptic climatology” discipline Weather patterns classified into N-different types Each type associated with a local climate response GCM weather patters matched to types, and assigned a local climate response

AIACC Workshop, Apr 2002 CSAG : University of Cape Town General categories of methodologies Transfer WeatherStochastic conditionedStochastic FunctionTypingon weather type Stochastic / weather generators calibrated on observed data, conditioned on atmospheric state. Very effective at capturing high frequency variance, peaks and extremes Requires long term data sets to effectively define stochastic characteristics Question of stationarity

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Note the underlying commonality: All methods are, in effect, algorithms to implement an analog. ie: each method simply draws a climate response from the historical record based on some atmopsheric state(s) from the same historical period Thus: why not implement a true analog? Simply match a given GCM field to all possible comparable fields in the historical and take the closest match?

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Evaluation of issues for effective empirical or statistical downscaling. 1) Choice of predictor variables Most commonly used are circulation related variables

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Evaluation of issues for effective empirical or statistical downscaling. 1) Choice of predictor variables 2) Predictor spatial representation Indices, EOFs, Synoptic classifications, Raw grid data Local versus remote (teleconnections) Surface versus upper air fields

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Evaluation of issues for effective empirical or statistical downscaling. 1) Choice of predictor variables 2) Predictor spatial representation 3) Antecedent conditions Local forcing as a function of antecedent events

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Evaluation of issues for effective empirical or statistical downscaling. 1) Choice of predictor variables 2) Predictor spatial representation 3) Antecedent conditions 4) Target (predictand) resolutions Station scale, impacts scale (scale of user community), RCM scale?

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Evaluation of issues for effective empirical or statistical downscaling. 1) Choice of predictor variables 2) Predictor spatial representation 3) Antecedent conditions 4) Target (predictand) resolutions 5) Training data periods Observational data that sufficiently spans the relationship for training downscaling function

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Evaluation of issues for effective empirical or statistical downscaling. 1) Choice of predictor variables 2) Predictor spatial representation 3) Antecedent conditions 4) Target (predictand) resolutions 5) Training data periods 6) Representing the climate change signal Predictors explaining significant variance may not be predictors sensitive to the climate change signal

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Evaluation of issues for effective empirical or statistical downscaling. 1) Choice of predictor variables 2) Predictor spatial representation 3) Antecedent conditions 4) Target (predictand) resolutions 5) Training data periods 6) Representing the climate change signal 7) Stationarity of function / predictors Is climate change primarily characterized by changes in frequency of existing events? Are changes in local sub-grid-scale forcing small with respect to synoptic forcing? Are residuals in downscaling from GCM-resolution due to low predictor resolution, or sub-grid scale forcing?

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Evaluation of issues for effective empirical or statistical downscaling. 1) Choice of predictor variables 2) Predictor spatial representation 3) Antecedent conditions 4) Target (predictand) resolutions 5) Training data periods 6) Representing the climate change signal 7) Stationarity of function / predictors A given downscaling implementation needs to take cognizance of, and evaluate, the dependencies

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Exploring the dependencies. Transfer function based methodology: gives dominance to synoptic forcing Challenging case: continental summer convective daily precipitation NCEP reanalysis 2.5 degree atmospheric predictors Station derived precipitation

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Exploring the dependencies. Transfer function based methodology (Neural nets): gives dominance to synoptic forcing Problematic case: continental summer convective daily precipitation NCEP derived predictors Station derived precipitation Regional context steep topography elevated inversions strong interannual variability Topography

AIACC Workshop, Apr 2002 CSAG : University of Cape Town January mean SLP January mean precip Dominance by semi-permanent high pressure systems with surface thermal trough Strong spatial gradients of precipitation strongly dependant on moisture transport

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Characteristic 7-day back trajectories into test region for downscaling (shading by specific humidity).

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Downscaling methodology: Transfer function methodology - derives local response as function of synoptic forcing, excludes sub-grid scale local forcing (useful for evaluation of dependencies). - Artificial Neural Nets (analogous to non-linear multiple regression) - derives non-linear transfer functions between NCEP (2.5°) atmospheric variables and precipitation (0.25°) 20 years of training data ( ) * Focus not on optimizing results, but a sensitivity study * Pre-1980 reanalysis data problematic for southern hemisphere

AIACC Workshop, Apr 2002 CSAG : University of Cape Town 1: Evaluation of predictor variables (20 examples) Surface700hPa500hPa temperaturetemperaturetemperature divergencedivergence divergencegeopotential height vertical velocityvertical velocityvertical velocity relative humidityspecific humidityspecific humidity u windu windu wind v windv windv wind Each predictor used independently to derive a transfer function to precipitation at 0.25°. Predictor temporal resolution: 12 hourly Predictor spatial resolution:9 grid cells (7.5° by 7.5°) centered on target location 48 hour antecedent predictor state included

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Predictor variableR Specific Humidity (500hPa)0.56 Vertical Velocity (500hPa)0.55 v wind (700hPa)0.53 Relative Humidity (Surface)0.53 Specific Humidity (700hPa)0.49 Divergence (700hPa)0.49 Temperature (Surface)0.45 Geopotential height (700hPa)0.44 v wind (500hPa)0.44 Divergence (Surface)0.41 Vertical Velocity (700hPa)0.40 Divergence (500hPa)0.35 u wind (Surface)0.34 u wind (500hPa)0.34 Vertical velocity (Surface)0.34 u wind (700hPa)0.34 v wind (Surface)0.34 Temperature (500hPa)0.30 Temperature (700hPa)0.27 Geopotential height (500hPa)0.19 Results suggest: Dominant relationship is with mid and upper troposphere humidity and predictors related to vertical motion.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Similar examination of other locations supports the above results. Suggests predictors should include mid-troposphere indicators of humidity and circulation dynamics.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Based on the above, a set of predictors may be chosen. eg: Surface temperature, u and v winds 700hPa specific humidity and geopotential heights 500hPa specific humidity and geopotential heights Trained function results: r = 0.7 predicted mean precipitation: 4.2mm/day observed mean precipitation: 3.8mm/day

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Residuals From either: Lack of information in predictors (choice or predictor or resolution) Local sub-grid scale forcing unrelated to synoptic state May be stochastically modeled (stationarity issues)

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Residuals Effect of stochastic addition of residuals to recover the higher frequency source of variance independent of the predictors Wmean: mean wet spell duration (number of days)

AIACC Workshop, Apr 2002 CSAG : University of Cape Town 2: Predictor Spatial Resolution Test relationship of target variable to atmospheric predictors progressively further away from region of interest. ANN downscaling using mid-troposphere (700hPa) specific humidity and geopotential height Predictors drawn from progressively larger regions: a) single NCEP grid cell co-located with target b) 7.5° by 7.5° window centered on target c) 15° by 15° window centered in target d) 22.5° by 22.5° window centered on target e) 30° by 30° window centered on target

AIACC Workshop, Apr 2002 CSAG : University of Cape Town 2: Predictor Spatial Resolution Test relationship of target variable to atmospheric predictors progressively further away from region of interest. ANN downscaling using mid-troposphere (700hPa) specific humidity and geopotential height Predictors drawn from progressively larger regions: a) single NCEP grid cell co-located with target b) 7.5° by 7.5° window centered on target c) 15° by 15° window centered in target d) 22.5° by 22.5° window centered on target e) 30° by 30° window centered on target Spatial resolutionr Single cell x x x x Increase in predictor window size, once large enough to represent spatial gradient, has minimal improvement.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town 2: Predictor Spatial Resolution Downscaling a function of information content in predictors -- a function of resolution. eg: Station daily rainfall downscaled from MRF (1°) atmospheric fields: Average error: 0.5 mm/day Suggests variance from sub-grid scale forcing is minimal in this case

AIACC Workshop, Apr 2002 CSAG : University of Cape Town 3: Predictor Antecedent State Test relationship of target variable to inclusion of the antecedent state of atmospheric predictors. Predictors used as: a) time coincident with target b) time coincident with target and increasing lag in 12 increments to 96 hours lag.

AIACC Workshop, Apr 2002 CSAG : University of Cape Town 3: Predictor Antecedent State Test relationship of target variable to inclusion of the antecedent state of atmospheric predictors. Predictors used as: a) time coincident with target b) time coincident with target and increasing lag in 12 increments to 90 hours lag. Lags of at least 24 hours are very beneficial

AIACC Workshop, Apr 2002 CSAG : University of Cape Town 5: Training data period Test sensitivity of downscaled function to data used in training. Case 1Train on 1980s -- test with 1990s Case 2Train on 1990s -- test with 1980s Case 3Train on 1982/83 -- test with 1980s and 1990s

AIACC Workshop, Apr 2002 CSAG : University of Cape Town 5: Training data period Test sensitivity of downscaled function to data used in training. Case 1Train on 1980s Case 2Train on 1990s Case 3Train on 1982/83 For each case, test function on independent decades. Case 1: Trained on 1980s, predicted 1980s:r = 0.66mean ppt: +7% Trained on 1980s, predicted 1990s:r = 0.59mean ppt: -9% Case 2:Trained on 1990s, predicted 1990s:r = 0.78mean ppt: +6% Trained on 1990s, predicted 1980s:r = 0.51mean ppt: +18% Case 3:Trained on 82/82, predicted 1980s:r = 0.33mean ppt: -34% Trained on 82/83, predicted 1990s:r = 0.11mean ppt: -28% Where training data spans the variability, performance good

AIACC Workshop, Apr 2002 CSAG : University of Cape Town 6: Representing the climate change signal Predictors that explain the most variance may not be the predictors that capture the climate change signal. Test: for each predictor, determine the climate change signal Train on the predictors, and predict from GCM control and future climate simulations

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Predictor variable Future - present downscaled % change Specific humidity (500hPa) 4.49 Specific humidity (700hPa) 4.71 Surface Temperature 2.43 Surface u-wind-5.47 Surface v-wind hPa geopotential heights hPa geopotential heights-1.63 Note: Choice of predictor may change sign of downscaled response 6: Representing the climate change signal Predictors that explain the most variance may not be the predictors that capture the climate change signal. Test: for each predictor, determine the climate change signal Train on the predictors, and predict from GCM control and future climate simulations

AIACC Workshop, Apr 2002 CSAG : University of Cape Town 6: Representing the climate change signal Downscaling using:Future - control: +2.1% Specific humidity (500hPa) Specific humidity (700hPa) Surface u-wind Surface v-wind 500hPa geopotential heights 700hPa geopotential heights Or excluding humidity:Future - control: -3.5% Surface u-wind Surface v-wind 500hPa geopotential heights 700hPa geopotential heights

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Spatial consequences Downscaled summer precipitation anomaly (future - present)

AIACC Workshop, Apr 2002 CSAG : University of Cape Town 7: Stationarity: Predictors: Do future synoptic events have present day representation Transfer function: Stability of relationship Sub-grid scale forcing: % contribution to local variance, feedbacks to atmosphere At a minimum, evaluate predictors...

AIACC Workshop, Apr 2002 CSAG : University of Cape Town 7: Stationarity Consider distribution of 700hPa geopotential height fields in GCM control simulation

AIACC Workshop, Apr 2002 CSAG : University of Cape Town 7: Stationarity Frequency of occurrence of each mode may be determined, and change under future climate calculated % change in frequency of occurrence from future-control simulations:

AIACC Workshop, Apr 2002 CSAG : University of Cape Town 7: Stationarity % change in frequency of occurrence from CSM future-control simulations: Similarity of future patterns to present day may be determined, and a measure of change in pattern calculated. % change in pattern from CSM future-control simulations: Where significant increases in frequency have occurred, variance of pattern modes has generally decreased. Hence: 700hPa geopotential height fields under a future climate are spanned by events in present day simulation

AIACC Workshop, Apr 2002 CSAG : University of Cape Town Some conclusions: Empirical/statistical downscaling has pragmatic attractions. Appropriate implementation can produce downscaled results consistent to changes in synoptic forcing. Care is needed!!!

Decision process to implement downscaling Preparation Are you looking for a sensitivity study, projection, or probabalistic prediction : What is needed versus wanted versus realistic? Temporal: Resolution and duration. ie: hourly, daily, monthly seasonal, etc., and 1 year through to decadal etc. Spatial: Resolution and domain. Ie: point or station scale, through regional scale / areal average. Variable: Direct or derived (eg: temperature versus storm surge)? If multivariate, is phase matching between variables important? Do you need statistics or time series? Source: Has/is an appropriate solution available elsewhere? Has it been evaluated? Baseline data: What is available (and when)? Does it match all above requirements? Are any shortcomings to the above critical? What are their implications?

Decision process to implement downscaling GCM data Which GCM(s), from where, and how/why are you selecting them? When will they be available? What SRES or other forcing scenario(s) are used? Are native temporal and spatial resolutions appropriate to the task? (Recognize skill level is typically > 7-9 grid cells) Validation (Evaluation): (Essential for understanding what you get in the end!) Has the GCM been evaluated at the spatial/temporal resolution of intended use? If not, how will you evaluate it? To what degree is the GCM future climate statistically stationary? What skill level/margin of error is acceptable?

Decision process to implement downscaling Resources What computational hardware resources are available? What are your own/team IT skills (programming, script writing, only point and click, system administration, data handling, etc)? Decision process to implement downscaling Choice Do you need to downscale, is direct GCM output ok, is applying a GCM anomaly field to a baseline climatology ok, is interpolation ok? If so, do it! Choose appropriate downscaling method based on answers above RCMs or Empirical/statistical

Decision process to implement downscaling RCM Regionalization Which model and why? How fast will it run under available resources? Will it even run on available resources? Will areal averages (!) be what you need? What spatial resolution and number of levels. What map projection. Domain selection and domain sensitivity. Has a baseline climatology been run? What boundary conditions were used? What duration used to derive the climatology? Land surface scheme -- what choice? Model tuning, has it bben tuned, how and why? Is stationarity of parameterization important? Validation (evaluation): (Essential -- especially w.r.t feedbacks!) How was/will baseline evaluation done (see under GCM)? Are the errors acceptable -- do they induce larger problems? Nested control run climatology -- how long (long enough?) What are the errors, are they acceptable? Be very careful here, paying attention to feedback processes. What is the domain topography like -- is the model hydrostatic, does it need to be non-hydrostatic? What is the driving : nested resolution ratio? Is domain large enough to recapture sub- GCM grid-scale variance over domain of interest? Feedbacks: are they recognized? What degree of consequence will ignoring them have on results? (eg: changing veg) What is the synoptically forced versus locally forced variance ratio. What is the signal to noise ration of the climate change anomaly?

Decision process to implement downscaling Empirical downscaling Note: Can ONLY generate predictand varinace that is inherent in the cross scale relationship with GCM-scale data. The rest is "made up"!!! Predictor-predictand relationship: how strong is it? Are the required predictors available from the GCM? Does the training/validation data adequately span the variance structure of the climate system? Is the "synoptic" forced predictand variance enough for the application, do you need to recover locally forced variance? Do the predictors carry the climate change signal? Is the relationship strongly non-linear? What domain size and temporal resolution/duration of the predictors? Are the GCM predictors stationary, can the degree of non-stationarity be accepted? Is multivariate phasing important? Method: Weather generator, transfer function, weather typing, true analogue, some combination? Do computational and IT resources meet the methods requirements. Predictor pre-processing -- yes, no, how, why? (eg, EOFs etc). Are the pre-processed predictor forms stable -- eg: are the EOFs or climate indices of the training data valid under future climate? Validation: how will you validate the training procedure? Independent test versus training data -- where do they fall within data space? What are the residuals, and biases after training? Should/do the GCM predictor field need bias correction? Downscaling function stationarity: can it be tested or evaluated?