Microphysics evolution, Past, ongoing and foreseen Part b: present status and foreseen evolutions (species handling, processes, geometry, layers split) R. Brožková, J.-F. Geleyn A1WD, Ljubljana, Slovenia, 13-15/06/2012

Set up of first sensitivity study ALARO Microphysics is used in a unified stratiform and convective case (the 3MT scheme), in grey zone: –Resolution 4.7 km; 87 levels; –10 days suites with the assimilation cycling, for highly active convective period of 21 to 30 June Experiments (with respect to ALARO reference, going progressively to ARPEGE choices): –Switch off pseudo-graupel; –ARPEGE type of processes; –Lagrangian sedimentation and fixed speed of precipitations.

Cloud liquid water – average over space/runs at +48h Alaro reference No pseudo graupel; slower fall, more water in feedback ARPEGE Processes Lag. sed. & fixed fall speed: faster fall higher up, less cloud water in feedback

Cloud ice water – average over space/runs at +48h Alaro reference No pseudo graupel ARPEGE Processes Lag. sed. & fixed fall speed

Rain-drops amount – average over space/runs at +48h Alaro reference No pseudo graupel ARPEGE Processes: diff due to evaporation. Lag. sed. & fixed fall speed – compensating the evaporation effect.

Snow-flakes amount – average over space/runs at +48h Alaro reference No pseudo graupel – impact noticeable ARPEGE Processes - little change for snow Lag. sed. & fixed fall speed: higher speed diminishes snow

Precipitation structure - night ref A bit spread no graupel Less rain, mainly evap. ARP processes More rain SLAG, fix speed

Rain-drops amount – average over space/runs at +18h Alaro reference No pseudo graupel ARPEGE Processes Lag. sed. & fixed fall speed: in the afternoon rain is considerably enhanced

Precipitation structure - day ref similar no graupelARP processes A bit less rain SLAG, fix speed More rain everywhere

Seen by budgets Difference of budgets due to sedimentation, which influences all other processes

Seen by scores (1) Humidity scores at 250 hPa against radiosoundings: ALARO a bit better tuned for convective regime Wind at 10m: ARPEGE tuning is better.

Seen by scores (2) Change in sedimentation is clearly seen in bias of precipitation in the afternoon. Higher bias in cloudiness show that ARPEGE processes lead to moister atmosphere. Once the sedimentation is switched to lagrangian with fixed speed, there is a compensation.

Species at 9km and 4.7km with 3MT Suite_1 at 4.7km and Suite_2 at 9km, average over the same domain and time period for the afternoon (18h). Differences are within those of changing microphysical processes and/or sedimentation.

Influence of geometry Convective part of 1h precipitation sum, afternoon. Left: maximum-random overlap; Right – pure random overlap. In the picture to the right, too much seeded surfaces, thus too much evaporation and hence too small amounts of precipitation.

Influence of convective condensate protection Convective part of 1h precipitation sum, afternoon. Left: protection applied; Right – no protection, less pronounced maxima.

Some (partial) conclusions n Usefulness of the modularity => we may detect what would be otherwise unnoticed due to compensations and feedbacks; n Parameterization of sedimentation – bigger effect than changing details in processes? n Multi-scale performance of the schemes – uneasy but not impossible; n Grey zone parameterization of deep moist convection – feasible; geometry and prognostic aspects are key ingredients.

Foreseen evolutions n Given the difficulties linked with the diagnostic graupel (and in order to prepare other evolutions), we would like to use prognostic graupel (at unchanged phenomenology). Work is ongoing (Joris Van den Bergh). n After this step, we could program a functional equivalent to the ICE3 microphysics package of AROME under ACACON, ACCOLL and ACEVMEL. Technically the work is without big hurdles. But to make the (long time- step oriented) algorithms compatible with the ALARO and ARPEGE ones will require some special attention. Hence the perpetual postponement of the task for the past 3 years … n At the level of the current geometry one should have the third option (like for the diagnostic radiative clouds of Christoph Wittman) of an intermediate between random overlap and maximum-random overlap. The work is now at the validation stage. n The core issue will likely be the one of the layers split. n For both last items, let us start with the existing:

Geometry of clouds and rain (1/4)

Geometry of clouds and rain (2/4) Random overlap of parts separated by clear air, maximum overlap of adjacent parts (schematic view) Intuitive solution: 2 inputs and 2 outputs for the transmission/creation in the considered level (the cloud homogeneises the precipitations output) ?? This is now the correct solution with 4 inputs and 3 outputs (the cloud still homogeneises). But why is there input in the time- step non-seeded parts ? Because there was a cloud there in a previous time-step and that the precipitations it generated did not finish falling (if not evaporating)

Geometry of clouds and rain (3/4) Two options are currently coded: - Maximum overlap of clouds (more realistic) – reference; -Random overlap of clouds – exp 1 The impact (here shown for evaporation of falling species) is not negligible. The problem cannot be treated as linear.

Geometry of clouds and rain (4/4) Far more complex than for radiation, but still tractable ! Maximum-random case ( =1), C & C * local & above cloudiness (clear part), (cloudy part) seeded proportions, * above again Random case ( =0) 0 < < 1

Treble cloud, a more realistic view of the « h+v » geometry (1/2) The continuous spectrum of cloud intensity is replaced by two synthetic parts, instead by the current mean value. There are now more transition possibilities from layer to layer (4=>9). Overlap hypotheses are even more crucial. After Shonk et al.,

Treble cloud, a more realistic view of the « h+v » geometry (2/2) Ultimate sophistication, the decorrelation depth depends on the latitude (continuous tropical clouds are more vertical than frontal ones). Here also a decorrelation factor exists for the non- random part, parameterised in function of the layer depth (we could already have that). Beware that what is named here is in ALARO notations (and is then something else)

Conclusions n Key factors in microphysics, judged from the impact on results: –Geometry of clouds and seeded parts; –Sedimentation – fall speed; –Evaporation/melting (processes) => Phase changes (latent heat) and moistening – dominating factors? –Autoconversion rate (feeding precipitation) n Goal: –get correct results for good reasons – physical realism: go for better descriptions of cloud geometry etc; –Attention on compensating mechanisms, over-tunings and/or arbitrary tricks (penicillin vs acylpyrin).