Model Error Diagnosis across Timescales

Transcription

1 Model Error Diagnosis across Timescales Sean Milton THORPEX PDP Workshop Diagnosis of Model errors

2 Traceable Model Error Diagnosis Framework Characterizing Model Systematic Errors across timescales (Study Error Drift). Routine Metrics (NWP, THORPEX, Seasonal, Decadal). Phenomena/Processes - Stormtracks/Blocking, MJO, Monsoons,... Identify errors in (Diabatic & Frictional) parametrized forcing (Local Physics). Vs. Observations Field Experiments (e.g. VOCALS, GERBILS, AMMA,CEOP) & EO. NWP framework - Physics tendencies Hi-resolution, cloud resolving models (CASCADE) (Scale Interactions) Feedback between errors in parametrized processes & (errors in) large-scale mean flow and variability. (Local vs Remote Forcing, Fast vs. Slow Physics Processes). Relaxation, trajectories, dynamical core, aquaplanets, vorticity balance models. Parametrization sensitivity experiments (trialling new schemes) (not tuning!). Role of resolution (numerics) in model systematic errors across timescales TRANSPOSE AMIP. Role of Coupled Processes & Earth System Complexity in Model Error Ocean, Land, Ice, Aerosols/Chemistry.

3 Resolution Unified Modelling High resolution Limited Area (LAM, RCM, CASCADE) Atmosphere NWP (to 16km by 2011, L70) Understanding regional processes Seasonal/Decadal Global Coupled Climate (~60km, L85) Multidecades on joint supercomputer Monthly to Decadal Forecasting Complexity Centennial/Ensembles Global climate (~ km, L63) Multicentury runs/ S2D ensembles Projections to 2100 Resolution Earth System Global Climate (~150km,L63) Multicentury ensembles, rapid response long timescale feedbacks Simple models Energy Balance Models Statistical sampling Exploring scenarios JULES: Coupling the Land Surface

4 Characterizing Errors Seamless examples.

5 Resolution Timescale Systematic Errors NWP to Climate N96 Decadal N320 5 Days

6 Precipitation Annual Means Global NWP vs HadGEM2-A

7 ASCOS Aug 2008 C.Birch, U.Leeds

8 ASCOS Evaluating Processes HadGEM1 Climate Model UM=0.003

9 Diagnosing Error Sources Asian Monsoon Examples

10 Monsoon Precipitation & Low Level Flow Errors A A

11 Day 1 Forecast Errors JJA 2009 TRMM Day 1 FC Strong convergence (northward flow) at coast Excessive convergence CIO low level Kelvin wave response to enhanced model heating? Day 5 FC

12 JJA 2009 Momentum Budget Model Layer 1 First 24 hour Contribution Of each forcing to residual still Difficult to diagnose

13 What Next? PV balance trajectories Comparison with ECMWF Statistical analysis of time varying error & forcing DP Dt 1 1. F. a V wind error 5-20N, 70-75E GWDu 77E,10N

14 Physics & Resolution sensitivity across timescales.

15 Example of Recent Improvements to Tropics Cycle G39 (Mar 2006) Parameterization changes in the UM Convection Scheme : Adaptive Detrainment (Derbyshire et. al. 2009) Reduced Tropical Humidity & Temperature Biases. Improved vertical heating profiles and precipitation. Change to Marine BL latent heat flux at high wind speeds (Edwards, 2008) reduced evaporation over Oceans - > reduced precipitation. Non-local momentum mixing in convective BL (Brown et. al. (2009)) reduced wind biases in tropical BL reduced evaporation Day 5 T bias in Tropics New Physics G39 Control

16 Model Physics Sensitivity Across Timescales. Cycle G39 (Convection, BL) ENSO - Observations (GPCP, HadISST) NWP HadGEM1 HadGEM2 Clim Martin et.al. 2010, JClim

17 Traceable Model Hierarchy Impact of Resolution & Stochastic Physics Developed N216 (60km) L85 Atmosphere for climate Developed NEMO-CICE O(0.25)L75 model The coupled N216L85O(0.25)L75 model has been set up and run. Investigating the role of resolution on the mean climate, modes of variability and extremes across resolutions We have developed an N48L85 model and implemented the SKEB stochastic physics scheme Malcolm Roberts

18 Effects of horizontal resolution on ENSO Observations HadGEM1 HadGEM3 N96 HadGEM3 N216 Sarah Ineson

19 Effects of vertical resolution on ENSO teleconnections Observations HadGEM2 L38 HadGEM3 L85 See Ineson and Scaife, Nat. Geosci., 2009

20 Summary Systematic Errors have reduced across all timescales experience of NWP centres. Large systematic biases persist particularly in tropical mean/transient/intraseasonal circulation, Blocking & hydrological cycle progress is slow. Systematic errors traceable across timescale Use day 1 error to explore local physics errors (TRANSPOSE AMIP) still challenging! Use of Observations & HiRes (~1km) modelling (CASCADE) - to study parametrization errors. Better tools required to study interaction of local physics and large-scale circulation across all time and space scales relaxation experiments, Lagrangian budgets, idealised modelling.more LS. dynamical insight/expertise Systematic errors in coupled systems ocean/land/atmosphere/cryosphere/biosphere Techniques? E-S complexity Aerosols, chemistry at weather & climate timescales

21 Sub-Seasonal to Seasonal

22 Model Diabatic and Frictional Forcing JJAS 2003 Case Studies DP Dt 1 1. F. a

23 Backward Forecast Analysis Forward Forecast Analysis

24 Prince Xavier Coupled model errors: Using seasonal forecasts Largest seasonal hindcast errors over Indo-Pacific warm pool a region of strong air-sea interactions, initiation of MJO Error develops at day 4-5 an propagate across timescales Investigate the timescales at which initial MJO errors develop in seasonal hindcasts and THORPEX runs Understand the causes of initial errors in specific MJO cases (collaboration with CR and the NWP staff) Look at impact of physics and horizontal resolution changes in HadGEM3 assessment runs

25 OLR Anomaly Seamless Prediction of MJO MOGREPS-15 THORPEX-TIGGE ensemble (Ann Shelly) GROWTH OF ERRORS - Composite OLR anomalies (analysis & forecast) 15N -15S in phase 1 of the MJO at Day 1 and 10 of forecasts from DJF 2006,2007 and WHEELER & HENDON INDEX : MOGREPS member ensemble - N144L38 resolution o x.83 o 3 DAY 1 Well predicted DAY 10 Weaker MJO AN FC D TELECONNECTIONS - Rossby wave propagation in 250hPa streamfunction anomalies. Teleconnections weaker in forecast as phase 1 heating (dashed) is weaker than analysis at Day 10 Outer shading all members Inner shading 50% of members Spread tends to increase with lead time PREDICTABILITY - MOGREPS-15 vs. a statistical model (Maharaj and Wheeler) MOGREPS errors remain lower until day 13. Suggests greater predictability in dynamical model out to 13 days.

26 Climate Timescales

27 Traceable Climate Model Hierarchy: Blocking & Resolution N48 blocking significantly less than N96 N216 shows little improvement on N96 Reflects lack of increases in TKE under investigation Claudio Sanchez

28 High resolution climate modelling of extremes 12km 4km 1km Gain understanding of extreme rainfall processes across different space and time scales. Identify model deficiencies at varying resolution, and apply understanding to inform climate model development. Examine how precipitation changes with global warming in a convection-permitting model, and hence assess reliability of future projections in current climate models.

29 High resolution climate modelling of extremes: Experimental design Inner domain spanning sub-region of the UK at 1.5km resolution, 70 vertical levels based on operational UKV SE_UK: inner domain 200x200 Variable resolution blending zone at boundary, from 4 to 1.5km Nested within 12km regional climate model, driven by ERAinterim (for validation) and 60km global atmosphere-only model (for control and future scenario runs) S_UK: inner domain 350x300 2 x 10 year simulations for control and future periods Convection scheme switched off Lizzie Kendon Boundary of 4km rim Boundary of variable resolution (4->1.5km) region Boundary of 1.5km inner domain

30 High resolution climate modelling of extremes: Progress to date SEUK model run successfully for ~ 2 days using ERA40 boundary conditions. Runtime ~ 1hr -> Suggests larger SUK domain may be possible Problems with ancillary updating in variable resolution model resolved Ready to start runs for JJA 2007 season using NAE boundary conditions (Retrieval and post-processing of NAE boundary data nearly complete) CPU cost of decadal length runs (Estimate ~3-6 months dependent on domain size) Lizzie Kendon SEUK model orography (E->W)

31 Traceable Model Hierarchy: Progress We have developed an N216L85 HadGEM3-A model which has been run for 20 years We have agreed on a common set of ocean vertical levels (75) across FOAM, CR and NOCS We have built a NEMO-CICE O(0.25)L75 model and performed runs with observed atmosphere forcing The coupled N216L85O(0.25)L75 model has been set up and run for 3 months We are investigating the role of resolution on the mean climate, modes of variability and extremes across resolutions We have developed an N48L85 model and implemented the SKEB stochastic physics scheme

32 AQUM example

33 Spare

34 Budget Diagnostics Following Klinker and Sardeshmukh (1992) DX Dt OBS 0 Observations - Long-term averages of the tendency in temperature, moisture, momentum or PV should be close to zero DX Dt MODEL DX Dt PARAM DX Dt DYN Model tendencies not zero due to model error growth from parametrizations or dynamics. Budget residual due to model error Examine the budget residual and all terms in budget from very short range forecasts to try and determine local sources of error growth in parametrizations or dynamics.