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Motivation Configuration Results Long-term statistics of continuous single-column model simulation at Cabauw Roel Neggers Pier Siebesma thanks to many others at KNMI
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Idealized observed situations, considered typical for a specific weather regime Evaluation against observations and LES simulations SCM case studies (GCSS BLCWG, ARM CMWG) Dry CBL Wangara GABLS3 Stratocumulus FIRE ASTEX DYCOMS M-PACE Shallow cumulus BOMEX ATEX SCMS RICO ARM SGP Deep cumulus TOGA COARE LBA ARM SGP TWP-ICE Purpose: improvement of the parameterization of fast sub-grid processes (e.g. turbulence, convection, clouds, radiation) Advantages: computational efficiency & model transparency
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Potential issues: i) How representative are these idealized situations? ii) Parameterizations might get calibrated to rare situations iii) Do the available cases represent those situations where GCMs have most trouble / uncertainty ? Q: How can we improve/ensure the statistical significance and relevance of SCM simulations? This question is relevant for both GCSS and CFMIP: GCSS: to better connect to GCM problems CFMIP: need for insight at process level
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Continuous SCM evaluation – The Cabauw SCM Testbed Purpose: Daily SCM simulation at Cabauw for long, continuous periods of time Evaluation of long-term statistics against observational datastreams
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The idea * Short-range (3 day) SCM simulations are generated daily for Cabauw Method: a combination of prescribed large-scale forcing and nudging towards a background state (observed/forecast/reanalysis) * Build up a long (multi-year) archive of simulations Allows diagnosing monthly/yearly statistics: i) improved statistical significance (representativeness) ii) many different weather regimes are automatically captured iii) a fair comparison with similarly diagnosed GCM statistics * Comprehensive evaluation of the complete parameterized system against Cabauw observations Covering thermodynamics, momentum, radiation, clouds, soil, etc. Allows constraining all parameterizations simultaneously should reveal compensating errors in GCMs
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The Cabauw site Operated by KNMI Operational since 1972 Tower height: 213m Main scientific goals: * Atmospheric research (PBL) * Climate monitoring * Air pollution monitoring * Model evaluation Cabauw is a site http://www.cesar-observatory.nl/ remote sensingin situ (in tower)in situ (ground) wind profilerSJAC2m meteo CT75 ceilometerLAS-Xrain gauges ir-radiometeroptical particle counterdisdrometer 3 GHz radarFSSP-95TDR 35 GHz radarnephelometerBSRN station 10 GHz scanning radarsonic anemeter backscatter lidargas analyzer GPS-receiveraetholometer HATPRO MWRsun photometer UV radiometerhumidograph scintillometerwind sensors pyranometer nubiscope temperature sensors
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What are the strong points of Cabauw for model evaluation? * The number of operational instruments * Continuity of measurement * Long time-coverage * High sampling frequency * A well-organized data archive that is easily accessible (CESAR)
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Testbed infrastructure: the interactive browser
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Individual cases Example: a diurnal cycle of shallow cumulus convection ◊ : CT75 lowest cloud base
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Evaluation strategy 1) Statistically identify a problem in a GCM Long-term GCM statistics guide the evaluation effort 2) Assess if the problem is reproduced by the corresponding SCM Exactly matching the GCM statistics (monthly/yearly means) 3) If so, identify which individual days contribute most to the error Selected individual cases are guaranteed to matter 4) Study those days in great detail, using a variety of statistical tools 5) When the cause is identified and understood, formulate a solution 6) Re-simulate and re-evaluate the modified SCM 7) Rerun the GCM including the improved physics 3D 1D
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Example Addressing a summertime diurnal warm bias over land in a GCM An issue encountered during the implementation of a new shallow cumulus scheme into the ECMWF IFS EDMF-DualM Eddy Diffusivity Mass Flux scheme Teixeira and Siebesma, AMS BLT proceedings, 2000 Siebesma et al., JAS 2007 Dual mass flux framework Neggers et al., JAS 2009, June issue
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ECMWF IFS difference in summertime diurnal cloud cover between CY32R3 + EDMF-DualM and CY32R3 Thanks to Martin Köhler, ECMWF Step I: The GCM problem free climate run, June-July 2008
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A difference in daily mean 2m temperature over land free climate run, June-July 2008
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Q: can the Cabauw SCM Testbed provide some evidence? Hypothesis: 1. less PBL clouds 2. larger SW down 3. larger H 4. low level warming SW
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Step II: Do the SCMs reproduce the GCM behavior? cloud cover at noon differs by ~20% 2m T differs by ~0.5K CY31R1
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~50 W/m2 (Note: there are still some issues with this observational datastream) Related monthly-mean differences support our hypothesis CY31R1
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Step III: Conditional averaging Important result: model differences are gone! -> proof that clouds are the cause CY31R1 a) clear convective days in May 2008
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b) shallow cumulus days in June 2008 Differences amplify –> this regime is the cause ~100 W/m2 CY31R1 CY31R1 is too optically thick, while DualM is too transparent
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Step IV: Zooming in on single days Which days contribute most to the monthly-mean differences? CY31R1
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CY31R1 tends to produce anvils at the top of the cumulus PBL 17 June 2008 CY31R1 dualM_20080429_buoysort
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Different tendency to form cumulus anvils is caused by differences in the vertical structure of model mass flux: MM Non-mixing; Fixed structure Mixing; Flexible structure Tiedtke (1989) in IFSEDMF-DualM
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Step VI: Modified SCM 2mT bias has reduced Note: it has not completely disappeared CY31R1 Now that we understand the problem, we can make targeted changes Much better SW down
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A significant monthly-mean difference! The plot thickens… The surface Bowen ratio (H/LE) CY31R1 dualM_20080429_buoysort
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CY31R1 dualM_20080429_buoysort What can change the surface Bowen ratio? Could it be the surface precipitation?
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20 June 2008 a diurnal cycle of shallow cumulus convection cloud fraction precipitation flux CY31R1
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dualM_20080429_buoysort CY31R1 dualM_20080429_buoysort CY31R1 dualM_20080429_buoysort CY31R1 shallow cumulus surface rain is i) stronger and ii) more intermittent Note: no rain was observed This boosts the interception reservoir (a.k.a. “skin humidity”) This actively increases the surface latent heat flux, at the cost of the sensible heat flux
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We have to refine our hypothesized impact mechanism: it’s more complex, involving both a cloud-radiative feedback as well as an atmosphere-land feedback 1. less PBL clouds larger SW down larger H low level warming SW higher rBowen 2. less surface precip
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Sensitivity experiment: Re-run DualM for June 2008 with an artificially boosted surface precipitation supply to the soil model CY31R1 dualM_20080429_buoysort dualM_20080429_buoysort - Pboost CY31R1 dualM_20080429_buoysort dualM_20080429_buoysort - Pboost
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Step VII: Modified GCM Monthly mean SCM results translate to IFS: A beneficial impact on 2m T over land!
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Summary It is possible to reproduce long-term statistics of GCM behavior with continuous daily SCM simulation Identifying the individual cases that contribute most to the time-mean SCM error ensures we study the most relevant situations Using the Cabauw SCM Testbed we were able to understand and address several important issues encountered during the implementation of a new shallow cumulus scheme into the ECMWF IFS Conditional averaging can be a helpful tool in understanding model behavior The availability of continuous, long-term observations of all components of the parameterized system is essential The SCM testbed method can be used to identify and improve interactions between climate system components in GCMs
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Outlook Top priority: to make the testbed server publicly accessible More SCMs ECHAM5 HIRLAM/AROME/HARMONIE UK MetOffice COSMO More locations Cloudnet sites ( Chilbolton, Lindenberg ), ARM sites More observational datastreams Nubiscope, UV lidar, soil measurements, profiler Improved spatial coverage Surface instrument networks, satellite datasets, scanning radar Score metrics (RMS, Brier scores)
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