Challenges in model development

Transcription

1 Challenges in model development Andy Brown 29/6/10

2 Contents How do we try to improve a model? Bottom up Top down Examples (Sensitivity to drag)

3 Bottom up Develop new (and hopefully improved) parametrization e.g. based on theory, observations or cloud resolving model study Publish paper Put in full model (and fingers crossed) Tune This works pretty well..

4 Mass flux profiles in a range of humidities idealized modelling CRM SCM (UM4.5) 90% 90% 25% 25% Derbyshire et al., QJRMS (2004) EUROCS Special Issue

5 Single-step example of adaptive convection Model level Detrainment linked to fractional decline in buoyancy excess q v ex mass flux (control) mass flux (adaptive) Adaptive detrainment is a simple statistical model for partial detrainment of a cloud ensemble as it loses buoyancy

6 Impact of Cycle G39 on THORPEX fc JJA 2003 & Case Studies. Old day Bias Velocity Potential 250 Analysis New Old Reduced Divergence error at upper levels New (Cycle G39) day Bias

7 Top down Identify aspect of model results not happy with CBS scores (500hPa height cf other centres) Excessive pacific wind stress Poor Indian Monsoon Publish paper Stop Try new physics (or retunings of existing schemes) in hope that that will alleviate problem Link problem back to cause of error in a way which can guide which aspects of the model need to be changed HOW TO DO THIS MORE?

8 What can we try? Dynamical insight what sorts of scheme deficiencies would cause the symptoms we have? Seamless framework (transpose-amip) Carefully designed experiments (e.g. relaxation) Appropriate diagnostics Budgets Regime dependent errors Based on weather type (e.g. suppressed or active convection) Based directly on things to which the parametrizations are sensitive (e.g. stable or unstable boundary layer)

9 Drag parametrization Crown copyright 2009

10 Drag Model scores are sensitive to surface drag Why? What should drag be? How to unravel compensating errors?

11 Use of budgets: angular momentum Crown copyright 2009

12 Angular momentum budget m r uacos Relative angular momentum per unit mass (a=radius of Earth; =latitude) Integrate evolution equation vertically and average over complete latitude bands.. p * m t 0 r dp g TENDENCY 1 a cos p 0 * m v r h a cos p* a cos * cos x dp cos g SSO BL a RESOLVED OROG PARAM OROG BL * TRANSPORT (moves around angular momentum) SURFACE TORQUES (create and destroy angular momentum

13 Budget terms (10 18 Nm) Budget terms (10 18 Nm) Angular momentum budget January 2001, hour forecasts January 2001 T159 AMFC RES SSO BL Residual January 2001 T N Eq 90S N Eq 90S

14 (RES+SSO Torque) (10 18 Nm) (BL Torque) (10 18 Nm) (RES Torque) (10 18 Nm) (SSO Torque) (10 18 Nm) Mean differences from T511, Jan T255 T159 T N Eq 90S 90N Eq 90S N Eq 90S 90N Eq 90S

15 Which torques are correct? Total orographic torques between 20N and 50N show an undesirable sensitivity to resolution 14x10 18 Nm per 10 degree latitude band at T95 10x10 18 Nm per 10 degree latitude band at T511 Looking at resolution sensitivity of torques alone cannot tell us which is correct (low resolution too large or high resolution too small). try looking for resolution-dependent drifts in forecast angular momentum compared to analysis

16 E r (10 18 Nm) E r (10 18 Nm) Rates of change of relative angular momentum away from analysis (January 2001) 10 T T N Eq T95 T159 T511 T159 NO SSO 90S N Eq 90S Bigger torques at low resolution lead to loss of angular momentum But note comparable sized errors in other latitude bands where orographic torques are small should not try to interpret all errors as orographic

17 E p* (Pa/day) E p* (Pa/day) Rates of change of zonally-averaged surface pressure away from analysis (January 2001) 100 T T N Eq T95 T159 T511 T159 NO SSO 90S N Eq All simulations show tendency to move mass away from Equator over first day (worse in forecasts from ERA-analysis)....but problems worse at T95 which has the larger orographic torques Again suggests that it is the low resolution torques which are too large

18 Vertically integrated U (T+24) U (T+0) 60 N egs4 T+24 U ERROR, VERT INT 150 W 120 W 90 W T95 60 W 30 W 0 30 E 60 E 90 E 120 E 150 E N 2 egs4 T+24 U ERROR, VERT INT 60 E 80 E 100 E 120 E 50 N 50 N 40 N 40 N N 30 N S 30 S Local analysis of short range forecasts 60 S 60 S -2 can clearly identify larger 10 N 10 N problems associated with orography E 80 E 100 E 120 E 150 W 120 W 90 W 60 W 30 W 0 30 E 60 E 90 E 120 E 150 E N 30 N 20 N N t511 T+24 U ERROR, VERT INT 150 W t511 T+24 U ERROR, VERT INT But what is the cause of the smaller errors orographic drag? T E 80 E 100 E 120 E boundary layer drag? 50 N 50 N 120 W 90 W 60 W 30 W 0 30 E 60 E 90 E 120 E 150 E 3 40 N 60 N 60 N How do we disentangle compensating 30 N 30 N 1 errors? N 30 N 40 N S 30 S S 60 S N 20 N W 120 W 90 W 60 W 30 W 0 30 E 60 E 90 E 120 E 150 E N 10 N 60 E 80 E 100 E 120 E

19 Diagnostics relevant to parametrization: stability dependence of boundary layer wind errors Crown copyright 2009

20 Mesoscale model analysis cf observations Compared to observations, analysis winds are: Slightly strong by night Weak by day (especially summer) Station representivity? Errors in geostrophic wind? Neither can explain underestimate of size of diurnal cycle

21 Surface wind direction errors vs QuikSCAT : Northerly winds Smaller errors in NH (cold advection) Systematic negative error in SH (warm advection)

22 Diagnostics from full model support implementation of ideas that had previously come from process research Sharp tails over the sea in stable conditions reduction in mixing Near surface winds reduced and backed Non-local momentum mixing (Brown and Grant 1997) in convective conditions increased mixing increased near-surface winds

23 Boundary layer wind response to stability more realistic - reason to think drag more accurate

24 T2-difference DJF (ensemble of 6 integrations) Effect of MO-stability functions instead of LTG Effect of reducing z0 over land by factor 10 Contours at 1, 3, 5, K Courtesy of Anton Beljaars

25 Data assimilation over 20 days March 2004 Effect of MO-stability functions Why was this so bad? Response to excessive cooling? Loss of drag? And if so, drag on mean flow, or drag on eddies (cyclone spin-down)? Courtesy of Anton Beljaars

26 Where next? Model development is hard and has relatively small community engaged in it How to get more benefit from top down approach? In general? Specifically for drag issues? How can PDP community expertize be put to best use?