kilometre-scale um simulations of the indian monsoon (and ... · pdf filetimeseries of daily...

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Kilometre-scale UM simulations of the Indian Monsoon (and a little bit on Super-typhoon Megi)

Stuart Webster


Contents

•  Motivation for this study

•  Details of the UM Configuration

•  Simulation period, driving model details and limited area model configurations

•  Results

•  Rainfall and OLR

•  Animations, Time averages and timeseries

•  Sensitivity to Domain Size

•  12km and 8 km explicit and parametrized convection simulations

•  The high resolution tropical model configuration – what next?

•  Summary and Future Work


Motivation

•  Plot shows global NWP forecast rainfall errors. Points to note:- •  A growing wet bias over the equatorial Indian Ocean. •  A dry bias over India itself •  A wet bias over the Himalayas

•  Similar systematic biases are seen in climate simulations using MetUM. •  Uncertainty over how to parametrize multi-scale convection remains a fundamental barrier

to improving the simulation of the Indian Monsoon.. •  Here we aim to circumvent the problems associated with the parametrization of convection

by running high resolution explicit convection limited area model simulations of the Indian Monsoon.

•  Aims of this study are to assess whether high resolution simulations:- •  reduce the systematic model errors typical in the coarser resolution simulations which use the

convection parametrization. •  can be used as a “truth” to help understand and improve the current deficiencies in the convection

parametrization.

JJAS 2009 average


Details of the period of simulation and the driving model

•  All simulations run for 21 days starting 18th August 2011 00z. •  This was the most anomalously wet period over India of the 2011

monsoon. •  http://www.tropmet.res.in/~kolli/MOL/Monsoon/year2011/weekly_2011.html

•  Hence hopefully maximises impact of dry bias over India seen in the coarser resolution models.

•  Driving model details:- •  the global operational NWP model

•  N512, i.e. approximately 25 km horizontal resolution •  L70, 80 km lid levels set

•  Run as a series of 6 hour forecasts and reinitialised every 6 hours with successive operational analyses.

•  Aim is to keep lateral boundary conditions as close as possible to reality.


Limited Area Model Configurations.

•  All LAMs span the same domain and are nested directly inside the global model.

•  This includes the 1.5 km model •  through careful implementation of the lateral boundary conditions •  despite the factor of 25 jump in East-West resolution •  despite the extremely high orography of Tibet intercepting the

northern part of the domain. •  All LAMs initialised with the same data (the 18th 00z global model flow fields)

and thereafter free running. •  All forced with the same lateral boundary conditions, and with SSTs updated

daily using Met Office OSTIA analyses. •  Configuration therefore allows as clean an assessment of the impact of

horizontal resolution as possible

4500km

4500km

Model Climate Like NWP like 4 km 2.2 km 1.5 km

Horiz Grid 50 x 50 200 x200 1150 x 1150 2000 x2000 3000 x 3000 Vertical Levels L70, 80 km lid L118, 78 km lid Timestep (s) 1200 600 10 Convection Parametrized Explicit

Sub-grid mixing 1D BL 3D Smagorinsky

Critical RH 92% (z=0) ⇒ 80% (z≥3km) 99%



•  Explicit convection simulations. •  These are configured identically except for the mesh size.

•  Configuration is as per the currently operational UKV but with •  Changes as in the table above •  Accurate dynamics

•  minimises SLSL gravity wave damping •  3D potential temperature advection

•  Improved moisture conservation (“Qtidy”) •  Frictional Heating •  1. Why 3D Smagorinsky and such a short timestep?

4500km

4500km


Horiz Grid 50 x 50 200 x200 1150 x 1150 2000 x2000 3000 x 3000 Vertical Levels L70, 80 km lid L118, 78 km lid (L70,40km lid) Timestep (s) 1200 600 10 (50) Convection Parametrized Explicit

Sub-grid mixing 1D BL 3D Smagorinsky (2D Smag + 1DBL)

Critical RH 92% (z=0) ⇒ 80% (z≥3km) 99% (96 ⇒ 80%)




•  L118 levels set builds on the near surface fine resolution L70 (40km lid) levels set used in UK area high-resolution operational UM forecast models.

•  Gives 3 times finer resolution at 16 km c.f. L70 40km lid levels set

4500km

4500km




Critical RH 92% (z=0) ⇒ 80% (z≥3km) 99% (96 ⇒ 80%)



1.5 km model Technical issues •  Needed ~100 nodes to run

•  ~5 Tb of memory •  runs a little faster than real time

•  Generated ~2Tb of data per day •  That’s even with all the data being packed

•  e.g. winds written out to nearest 0.125m/s •  Start dump is 80Gb in size •  Output pp data files reinitialised every 30 minutes

4500km

4500km

Model Climate Like

NWP like 4 km 2.2 km 1.5 km

Horiz Grid 50 x 50 200 x200

1150 x 1150

2000 x2000

3000 x 3000

Vertical Levels L70, 80 km lid L118, 78 km lid

Timestep (s) 1200 600 10 Convection Parametrized Explicit Sub-grid

mixing 1D BL 3D Smagorinsky

Critical RH 92% (z=0) ⇒ 80% (z≥3km) 99%


Contents




•  Results








Rainfall and OLR animations

•  What is the impact of the model resolution on the simulation? •  Illustrate by showing animations of Rainfall and OLR.

•  Rainfall = colour, arrows=850hPa winds, contours = PMSL •  Instantaneous every hour

•  2. Rainfall I (1.5km, 24 km 120 km and N512 driving model) •  3. Rainfall II (1.5 km, 2.2 km and 4 km) •  4. OLR I (1.5km, 24 km 120 km and the N512 driving model) •  5. OLR II (1.5 km, 2.2 km and 4 km)

Rainfall Accumulations

•  All hi-res models coarse grained to 24km grid.

•  Comparing free-running simulations to driving (Global) model and TRMM.

•  Parametrized convection runs (panels (d) and (e) show:-

•  Excessive rainfall over the Equatorial Indian Ocean.

•  Dry bias over Central India.

(a) (b) (c)

(d) (e) (f)

TRMM data analysis + plot above produced

by Sean Milton

Rainfall Accumulations

•  Explicit convection runs (panels (a)-(c)) show:-

•  Reduced biases over Equatorial Indian Ocean and Central India.

•  However, these models appear to lack rain in areas of oceanic light rain.

(a) (b) (c)

(d) (e) (f)

TRMM data analysis + plot above produced

by Sean Milton

Timeseries of daily rainfall •  Domain Averages.

•  All models broadly capture the day-to-day trends over the 21 day period and generally lie within about 25% of the TRMM rainfall estimates.

•  4 km model rainfall is excessive for almost the full 21 day period.

•  1.5 km model is comparable to TRMM for the 1st week but then is approximately 20% drier than TRMM.

•  Analysing a 1 week long run would have been overly positive re-1.5 km model.

•  Obviously same caveat may apply for these 3 week long runs

Timeseries of daily rainfall •  Land Only Averages

•  Coarse resolution, parametrized convection models are clearly too dry

•  120 km (climate) model worst of all.

•  All explicit convection simulations approximately 25% too wet.

•  Significant dry bias replaced by a smaller wet bias!

Timeseries of daily rainfall •  Sea Only Averages

•  Opposite signal to that seen over land.

•  So explicit convection simulations too dry

•  Except for 4km model

•  Parametrized convection simulations too wet

•  E.g. 120km model wet bias replaced by an equally dry bias in the 1.5 km model.

Timeseries of daily rainfall •  Back to Domain Averages (top plot)

•  Already said that:- •  4 km model rainfall is excessive for

almost the full 21 day period.

•  All other models broadly similar because the change in bias over land is opposed by that over the sea.

•  4 km model behaves in same way over land as the other explicit convection simulations, but produces much more rain over the sea.

Time-averaged diurnal cycle

•  Driving model rainfall curves jump every 6 hours

•  As T+6 forecast replaced by T+0 from the next analysis

•  Parametrized convection runs show characteristic local noon maximum in rainfall.

•  Over sea as well as land.

•  Out of phase with TRMM over land

•  Correct phase over sea, but amplitude is much too strong.

•  Explicit convection simulations capture diurnal cycle much better over land.

•  ALL models have the same diurnal cycle over land AND sea.

•  TRMM land/sea diurnal cycles are out of phase.

Timeseries of domain averaged OLR

•  Domain averaged OLR plots show:- – Driving model is closest to satellite derived estimates. Reassuring since the flow

fields in this model are never more than 6 hours from a Met Office analysis!

– However, spin down of the driving model is evident from the large jumps at each analysis time in the diurnal cycle plot.

–  Parametrized convection runs remain close to observed values over the 1st week but then tend to have a 5-10 Wm-2 positive bias.

–  Explicit convection runs are persistently 25-30 Wm-2 too dark, with the 1.5 km model having the largest bias of all the simulations.

–  Sensitivity of the OLR to (the soon to be operational in the UKV) revised ice fall speeds will be shown on final results slide.

NOAA obs produced by Sean Milton


Contents




•  Results







Sensitivity of the simulation to Domain size

Simulations thus far Model Climate

Like NWP like 4 km 2.2 km 1.5 km

Horiz Grid 50 x 50 200 x200

1150 x 1150

2000 x2000

3000 x 3000


Timestep (s) 1200 600 10 Convection Parametrized Explicit Sub-grid


Critical RH 92% (z=0) ⇒ 80% (z≥3km) 99%

Sensitivity of the simulation to Domain size

•  2.2 km model just fits on 103 modes.

•  Looking here at 1st 14 days of simulation

•  As only just finished running

•  6. Rainfall + OLR animations

Model NWP like 2.2 km

Horiz Grid 400 x240

4000 x2600


Timestep (s) 600 10 Convection Parametrized Explicit Sub-grid


Critical RH 92% (z=0) ⇒ 80% (z≥3km) 99%

Large domain simulations currently underway

•  Both models produce excessive rainfall over central equatorial Indian Ocean rainfall.

•  Western equatorial Indian Ocean rainfall reduced (and hence improved) in 2.2 km model

•  Still to run 120km model simulation

Daily averaged Rainfall Accumulations

•  Comparison of the simulations over their common domain

•  Biggest impact is over the ocean.

•  Wetter in SW of domain shown and drier to the east of India (for the 2.2 km model at least)

•  Impact over India appears to be small

Daily averaged Rainfall Accumulations

BIG domain SMALL domain


Contents




•  Results








12km and 8km:- explicit and parametrized simulations

•  Original set of simulations shown above

4500km

4500km

Model Climate NWP like 4 km 2.2 km 1.5 km Horiz Grid 50 x 50 200 x200 1150 x 1150 2000 x2000 3000 x 3000

Vertical Levels L70, 80 km lid L118, 78 km lid Timestep (s) 1200 600 10 Convection Parametrized Explicit

Sub-grid mixing 1D BL 3D Smagorinsky

Critical RH 92% (z=0) ⇒ 80% (z≥3km) 99%



•  Explicit convection simulations. •  These are configured identically except for the mesh

size. •  In addition, 12 km + 8km use UKV critical RH

4500km

4500km

Model 12 km 8 km 4 km 2.2 km 1.5 km Horiz Grid 400x400 600 x600 1150 x 1150 2000 x2000 3000 x 3000

Vertical Levels L118, 78 km lid Timestep (s) 10 Convection Explicit

Sub-grid mixing 3D Smagorinsky

Critical RH 96% (z=0) ⇒ 80% (z≥3km) 99%



•  Parametrized convection simulations. •  These are configured identically except for the mesh

size and timestep

4500km

4500km

Model Climate NWP like 12 km 8 km Horiz Grid 50 x 50 200 x200 400 x 400 600 x600

Vertical Levels L70, 80 km lid Timestep (s) 1200 600 300 200 Convection Parametrized Sub-grid

mixing 1D BL

Critical RH 92% (z=0) ⇒ 80% (z≥3km)



•  7. Rainfall and OLR animations •  Parametrized convection:- 8km (and 12km) and 24

km simulations are very similar •  Explicit convection:- 12 km (and 8km) simulations

rather different to 4km simulation. •  12 km rain cells so intense that they

significantly alter the large-scale flow.

4500km

4500km

Model Climate NWP like 12 km 8 km Horiz Grid 50 x 50 200 x200 400 x 400 600 x600

Vertical Levels L70, 80 km lid Timestep (s) 1200 600 300 200 Convection Parametrized Sub-grid

mixing 1D BL

Critical RH 92% (z=0) ⇒ 80% (z≥3km)

12km and 8km models average diurnal cycle

•  OLR shows that convection representation dominates over horizontal resolution.

•  Parametrized convection simulations remarkably similar.

•  Explicit convection simulations show •  delay in timing of peak convection as move to

coarser resolution.

•  Significantly reduced rainfall over land relative to 4km model (which looks good).

•  Rainfall over sea more than doubles as go from 4km to 12km (which is BAD!!)

Thin lines = explicit Thick lines = parametrized

Daily Average Rainfall for 12km and 8km models

•  Parametrized convection simulations remarkably similar.

•  Explicit convection simulations very different

•  12 km simulation very poor. 8km and 24km parametrized

4km and 12km explicit


Contents




•  Results












•  Improved moisture conservation (“Qtidy”) •  Frictional Heating

4500km

4500km




Critical RH 92% (z=0) ⇒ 80% (z≥3km) 99% (96 ⇒ 80%)






•  Improved moisture conservation (“Qtidy”) •  Frictional Heating

4500km

4500km




Critical RH 92% (z=0) ⇒ 80% (z≥3km) 99% (96 ⇒ 80%)

What are we doing to unify the operational UKV and the tropical

convection configuration described here?

•  4km model, 7 day long simulations

•  OLR = daily averages

•  Rainfall = average diurnal cycle

•  OLR:- PS32 (which is soon to start) revised ice fall speeds reduce bias by about 10 Wm-2 (30-40%)

•  Rainfall:- •  Removing “Qtidy” increases domain averaged rainfall by

about 15% •  Consistent with Cascade results •  Revised numerical treatment does not work for

operational timesteps. •  Blended 1DBL + 3D Smagorinsky code (see Adrian Lock’s

talk) currently not working well for these simulations at 4km resolution.

•  8. What about the short timestep + accurate dynamics? •  ENDGame

OLR + Rainfall Timeseries


Summary

•  Within the UM framework, we now have the very powerful capability of very easily nesting a kilometre-scale LAM directly inside a global model

•  High orography in the boundaries is not an issue

•  These are fixed (so not variable) resolution LAMs

•  “Very easy” because technical issues taken care of by the UM “Nesting Suite”.

•  High-resolution simulations show •  More realistic temporal and spatial behaviour

•  Improved diurnal cycle over land

•  Reduced rainfall biases over both land and sea

•  Grey zone = 12km and 8km simulations

•  Nature of simulated convection dominated by its representation

•  i.e. whether it is parametrized or explicit

•  Gross errors in the explicit simulations shown here

•  Presumably dependent to some degree on choice of model configuration

•  i.e. run here as per the high-resolution models rather than as per GA4.


Future Work

•  Much more analysis of existing runs required.

•  Obviously 1.5 km resolution is convection permitting rather than convection resolving.

•  9. 200 m resolution simulation of the eye of Typhoon Megi

•  Still need to refine horizontal resolution by another order of magnitude to start to resolve convection properly.


Questions and answers

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