Super-parametrization in climate and what do we learn from high-resolution
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1 Super-parametrization in climate and what do we learn from high-resolution Marat Khairoutdinov Stony Brook University USA ECMWF Annual Seminar, 1-4 September 2015
2 scales-separation parameterized convection
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4 NWP Models Climate Models Initial conditions problem Confronted with truth everyday Boundary conditions problem No truth is known The only hope is physical realism (resolve everything!)
5 Radiative-convective equilibrium (RCE) NET TOA 301K (Present) 305K (Future) CRM 1Mom-Micro CRM+2Mom-Micro CRM+HOC (param) W/m2 Grid spacing, km Response to SST is not sensitive to microphysics; CRM+High-Order-Closure (HOC) SGS parameterization TOA reproduces Present, but not Present-minus-Future ; RCE with HOC has about twice as large equilibrium climate sensitivity (ECS) parameter; Coarse RCE with 4 km grid spacing appears to be the threshold when the ECS becomes invariant of the resolution SGS parameterizations can significantly alter climate sensitivity ERBE RCE omega500mb Tropics
6 Global CRM? Resolve everything! Great, but too expensive.
7 Super-parameterization roots from Single-Column Modeling (SCM) s t q t sω = sv + Q! #" ## p % 1 $ LSForcing Param ' s qω = qv Q!## "## p % 2 L $ Param ' s LSForcing Column-Physics Tendency (parameterizations; No horizontal scale Δx here) The large-scale forcing data would come from observations (GATE, TOGA, ARM, KWAJEX, etc.) All super-parameterization does is compute Q1 and Q2
8 Super-parametrization (SP) Multiscale-Modeling Framework (MMF=GCM+SP) Prototype MMF Approach: 2.8 ~ 300 km CRM columns x 4 km GCM Resolved Column-Physics (SP) Dynamics Step: CRM Forcing: sv sω dp = s* s n Δt CRM Tendency: CRM step (subcycling)
9 given MMF simulation is very expensive, with the but super-parameterization highly scalable is only supercomputers Simulated Years per Day Standard CAM Benchmark on Hopper (FV 1.9x2.5) CAM SPCAM # Processors from M. Branson (2013) Fig. clock used and spac for loga with effici the tests at th Com Bran
10 Super-Parameterization - Summary Runs like conventional parameterization: profile in, profile out; hence, the name, super-parameterization (term coined by David Randall); The CRMs do not communicate directly with each other ( embarrassingly parallel problem); Radiation is usually computed on CRM grid; no cloud-overlap assumptions are needed; Momentum tendencies are not generally returned to GCM due to wrong momentum transport by 2D CRM; however use of 3D CRM is possible; Surface fluxes are still computed on GCM grid; Tendencies due to terrain are also due to GCM (no topography in CRM); PBL parameterization is generally off for scalars, but not wind; The width of the CRM domain is not tied to the GCM grid size (same way as a convective parameterization using no Δx information); GCM grid-cell should be large enough to contain large-scale convective systems.
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12 Diurnal cycle of precipitation JJA Local Time of Precipitation Frequency Maximum Text SP-CAM SP-CAM Observations (Dai, 2001) CAM Common bias (early maximum around noon) of many climate models
13 Diurnal cycle of precipitation JJA Local Time of Precipitation Frequency Maximum SP-CAM T85 (1.4x1.4 o ) Observations (Dai, 2001) CAM We still don t understand why 4-km 2D CRM can do such a good job
14 Eastward propagation of MCSs over US CAM3 SP-CAM3 SP-CAM3.5 CAM5 SP-CAM5 T42 T42 2x2.5 o 2x2.5 o 2x2.5 o Eastward propagation is robust in SP-CAM even at T42! Only large-scale processes are responsible for propagation of MCSs. Kooperman et al 2013
15 Mean Precipitation over US Extreme CAM 2x2.5 o SP-CAM 2x2.5 o OBS SP-CAM is better than CAM to simulate the extreme precipitation Li, Rosa, Collins & Wehner, 2012
16 PDF of Rainfall SP-CAM vs CAM T85 SP-CAM does better job than CAM in simulating heavy rain rates Zhou and Khairoutdinov 2015
17 Change of today s extreme (99th) precipitation event frequency in RCP8.5 climate CAM 2x2.5 o SP-CAM T85 SP-CAM 2x2.5 o CAM T85 SP-CAM predicts much bigger increase in extreme precipitation frequency than CAM Zhou and Khairoutdinov 2015
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19 MJO in SP-CAM T21 CAM SP-CAM Randall, Khairoutdinov, Arakawa, Grabowski 2003 From the inception, SP-CAM/SP-CCSM has been arguably the best framework for MJO simulation
20 Intraseasonal Variability in Tropics Pritchard 2012
21 Inraseasonal Variability in Tropics SP-CAM MJO-event OLR anomalies NOAA OLR CAM MMF W 2 /m 4 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC NOAA NOAA El Nino La Nina Normal Seasonal Cycle of MJO MJO-event OLR anomalies Khairoutdinov, DeMott, Randall 2008 SP-CAM MMF W 2 /m 4 W 2 /m 4 El Nino La Nina Normal JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC NOAA
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23 Zonal cross-section of MJO Benedict and Randall 2008
24 Large increase of MJO in warmer climate Arnold et al, PNAS 2014
25 Self-aggregation of convection on sphere SST=const, Solar=const, f=0 Arnold and Randall (2015)
26 Arnold and Randall (2015)
27 Tropospheric moisture in Tropics binned by rainfall rate In Obs and SP-CAM, heavy rainfall corresponds to regions with high humidity, especially in low-to-mid troposphere. Is high sensitivity of precipitation to humidity the key for simulating MJO? Thayer-Calder and Randall (2009)
28 African Easterly Waves AEWs are well simulated in SP-CCSM, but virtually missing in CCSM SP-CAM predicts much stronger increase in extreme precipitation frequency than CAM T42 McCary, Randall, Stan 2014
29 African Easterly Waves OLR anomalies and 850 mb streamfunction and winds Zonal cross-section of moisture anomalies SP-CCSM couples convection and waves right to simulate AEWs even at T42! Again, as in MJO, mid-tropospheric moisture anomaly appears to be the key to simulating AEWs. McCary, Randall, Stan 2014
30 ENSO El Nino amplitude and periodicity is better simulated by SP-CCSM
31 Super-parameterized GCMs 2001: SP-CAM 2007: SP-fvGCM: NASA GSFC (Wei-Kuo Tao) 2010: SP-WRF: (Stefan Tulich) 2011: SP-CFS: Indian Institute of Tropical Meteorology 2014: SP-IFS: ECMWF
32 NOAA SP-CFS (IITM) CFS SP-CFS CFS SP-CFS Prec mm/d OLR, W/m2 OLR, W/m2 Goswami et al, JC (2015)
33 Thanks to SP-IFS - Super-parameterized IFS Anton Beljaars Peter Bechtold Filip Vana Glenn Carver First implemented in OpenIFS, which is a free running IFS (cycle 38R1), but without data assimilation system; Summer 2014: T159 (~1.125 o x o ) 3-year runs with SP-OIFS; Fall 2014: SP is implemented in IFS CY40R3. Fall 2014: SP is in IFS Single-Column Model CY40R1; Currently, implemented in CY41R3 and can be run using prepifs system.
34 Preliminary results using T159 SP-OpenIFS SP: 32 x 74; Δx=4 km; Δt=20s; All IFS cloud and convective parameterizations are off; PBL/mixing parameterizations are allowed; Radiation coupling through SP s mean profiles (not on CRM grid as done in SP-CAM); Free continuous climate run for 3 years starting Aug 2000.
35 JJA Precipitation T159 SP-OIFS GPCP (OBS) OIFS Mean climatology of SP-IFS doesn t look bad for a model which hasn't been properly tuned.
36 Frequency Spectrum (Subseasonal): Precipitation in Tropics (15 o S-15 o N) Symmetric IFS SP-IFS GPCP Anti-Symmetric IFS SP-IFS GPCP
37 Frequency Spectrum (S/N): Precipitation in Tropics (15 o S-15 o N) Symmetric IFS SP-IFS GPCP Anti-Symmetric IFS SP-IFS GPCP
38 Variance: day filtered precipitation Summer (May- Oct) Winter (Nov- Apr) TRMM IFS SP- IFS
39 Variance:&20+100&day&filtered&U850& Summer&(May+Oct)& & &Winter&(Nov+Apr)&& &ERA40& & & & & &&IFS& & & & & & &&SP+IFS& & & & & &
40 MJO$eastward$propaga*on$ Lag$correla*on$(U850,$Winter)$ ERA40! Reference$domain:$ 1.25SA16.25N,$68EA96E! *!US!CLIVAR!MJO!Diagnos5c!metrics! $IFS $ $ $ $ $$$SPAIFS$
41 Summer$ISO$northward$propaga*on$ Lag$correla*on$(U850,$Summer)$ ERA40! Reference$domain:$ 3.75N?21.25N,$68E?96E! $IFS $ $ $ $ $$$SP?IFS$
42 Tuning for cloud fraction using SCM IFS (TWP ICE case) IFS SP-IFS (control) SP-IFS (tuned)
43 Bias in OLR in SP-IFS CY40R1 forecast SP-IFS (before tuning) IFS
44 Bias in OLR in SP-IFS CY40R1 forecast SP-IFS (after tuning) ) IFS SP-IFS with reduced IceFall rate and CWP threshold
45 What have we learnt from SP? Even small-domain 2D CRM works better than current convective parameterizations to represent variability of climate system on various timescales. We know much more about MJO now thanks to the SP. As the SP interacts with a GCM as an ordinary parameterization (1D profile in, 1D profile out), it is in principle possible to develop a parameterization that works as well as the SP. Lots of MMF publications:
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