Idealized simulations of the diurnal cycle of deep convection using the new Met Office Cloud-Resolving Model (MONC) 1. Look at the memory properties in the deep convection 2. Investigate if the variability in the main state is important for the memory 3. Determine on what space and timescale we do have memory Chimene Daleu, Natalie Harvey, Bob Plant, Steve Woolnough CPPC: Met Office, Exeter, UK, 15-19 July 20191 MONC configuration Model dimensionality Domain Domain size size Horizontal resolution Horizontal Number ofresolution vertical levels Numberresolution of vertical levels Vertical Vertical resolution Model top Newtonian
Model top damping layer Wind shear damping imposed layer Newtonian Coriolis Wind shear imposed Boundary conditions Coriolis Boundary conditions 3D 200 m 200 99 m 99 On a stretched grid with more levels near the surface On a stretched grid with more levels near the surface 20 km , and 20 km None (relaxed to to with ) Zero Bi-periodic, rigid lid Zero
Bi-periodic, rigid lid 2 Setup and forcing are based on the EUROCS case study Control simulation Height (km) / 2 Peak SHF =130 LHF= 400 RC is prescribed: from 0-12km, then decreases linearly with height to at 15km Time (hr) Different strengths of surface forcing Weakly forced simulation: 0.5*Control Strongly forced simulation: 1.5*Control Peak SHF =65 Peak LHF= 200 RC= Peak SHF =195
Peak LHF= 600 RC= Most of the simulations are performed over 10 forcing cycles to ensure statistically significant results Most of the results presented are the composites over 9 forcing cycles, after the first forcing cycle has been removed 3 Evaluation on multi forcing cycles Control simulation ACu and km BCu or Cloud cores (ACu) Triggering ~ 3h Precip (mm/h) km Triggering =rapid intensification of convection with deep convective cloud top emerging rapidly into the upper troposphere Time (hr) Rainfall occurs too early after triggering=Early morning
precipitation peak Time (hr) Timing of convection for different strengths of surface forcing MRR= 0.1mm/h (weak), 0.2mm/h (control), and 0.3 mm/h (strong) Norm Precip MF and cloud fraction increases with the strength of surface forcing Time of triggering increases with decreasing forcing Time (hr) Triggering is at 2.75h (strong), 3h (control), and 3.5h (weak). Time (hr) After triggering: regardless of the strength of surface forcing deep convective cloud top emerges rapidly into the upper troposphere The rate of growth of cloud top is very similar Rainfall occur too early after triggering Precipitation peaks almost at the same time: about 1.5h after triggering Y (km)
Y (km) Evolution of rainfall events X(km) X(km) 2D surface precipitation (15 minutes output frequency) A grid point is masked as cloudy if Cloudy grid points are classified in clusters (rainfall events) PDF of (4-12h) The distribution is very broad. Number of rainfall events is Area of each rainfall event Average radius and standard deviation of radii 8 marked with a gradual growth of peaks at 4h (~1h after triggering) Between 4-5h decreases rapidly while is increasing
Mature stage(5-9h): adjusts from its peak value to a smaller value is decreasing Steady stage (9-12h): is almost constant continues to decrease and reaches 0 when precipitation stops () Growing stage(3-5h): Evolution of can be divided into three stages: ) ( Evolution of rainfall events
Hours does not vary substantially with time, away from triggering Time-evolution of the total MF is mainly caused by variations in the cloud statistics (number), rather than changes in the characteristics of the clouds (radius). 8 The mean MF per cloud increases with the strength of SF Growing stage: Clear size dependence and increase with the strength of surface forcing peaks 1 hour after triggering (for all cases) () Mature and steady stages No clear separations
) ( Sensitive to the strength of the forcing? Hours 9 Convection depends on its own history? [ (20 2 ,)] Evaluation of convection within a given area A Each area A is considered to have rain if its precip is Conditional probability of finding rain within a , Hours Persistence of rainfall events: Minimum persistence: 0 Maximum persistence: Hours For random distributions, the conditional probability of finding persistent rainfall by random chance: ]= There is no memory if
There is memory if Memory function = 10 Convection depends on its own history? [ (20 2 ,)] No convective memory if There is memory if Memory function = Newly developing rainfall events are more likely to persist for half an hour or so Mature (5-9h) and steady (9-12h) stages: Very similar memory functions Convection depends its history over the previous 3 hours Depending on the most recent history: It is more likely to rain where it was already raining or rainfall events are more likely to be suppressed if they have been active for few hours
already 10 Hours Growing stage (3-5h): Hours Lag Time (Hrs) Convection depends on its own history? sensitive to the strength of surface forcing? Lag Time (Hrs) Lag Time (Hrs) shows a weak sensitivity to the strength of surface forcing Weak: rainfall events decay less rapidly and are suppressed more strongly
sensitive to ? Very similar memory functions for A significant reduction when and 0 for 10 Lag Time (Hrs) Memory attributed to the initial thermodynamic fluctuations Study of Stirling and Petch  Onset of precip changes by several hours And rainfall amount is increased by 70 % (g/kg) +0.6 (g/kg) +0.3 In our study: Thermodynamic fluctuations at the start
of the next diurnal cycle The amplitudes of and are at least 6 times smaller Do they influence the evolution of convection on the15next day? -0.6 -0.3 (without changing the domain mean state) at all vertical levels between 15-24h Following homogenization perturbations: precipitation peaks at the same time Convection intensity is reduced by 10% (Mean rain rate= 0.18mm/h compared to 0.2mm/h in the control simulation) 15 Precip (mm/h) Memory attributed to the initial thermodynamic fluctuations 1- We applied homogenization perturbations of and Hours
Clear separations in the evolution of rainfall events is narrower and is smaller is increased (up to 450) Recovery time is over 6hours (3-9h) Convection intensity is reduced by 10% and N is increased Rainfall events are less intense N ( ) Following homogenization perturbations ) () ( Thermodynamic fluctuations have a significant impact of
the evolution of rainfall events Hours 15 Clear separations in the evolution of rainfall events is narrower and is smaller is increased (up to 450) Recovery time is over 6hours (3-9h) Convection intensity is reduced by 10% and N is increased Rainfall events are less intense Decay more rapidly Recover more rapidly (an hour earlier) ( )
Following homogenization perturbations and below 4km appear to contribute as a primary storage of convective memory N Homogenization perturbations below 4km or above 4km (Confirms the results of Stirling and Petch  and Colin et al. ) Hours Hours Lag Time (Hrs) 15 Lag Time (Hrs) Lag Time (Hrs) Summary We produced the Diurnal cycle experiment that focuses on the triggering of deep convection. Morning precipitation maxima (rainfall occurs too early after triggering) Rainfall events become relatively larger with increasing strength of surface forcing
Time-evolution of convection is mainly caused by variations in , rather than changes in The memory function depends on . Within a 20 Newly developing rainfall events are more likely to persist for an 0.5h. Depending on the most recent history of convection It might be more likely to precipitate where it was already precipitating or Rainfall events might be more likely to be suppressed if they have been active for few hours already Thermodynamic fluctuations at the start of the next diurnal cycle have A little impact on the timing and intensity of convection A significant impact of the evolution of rainfall events N decreases (up to 450 reduction), R and increase Rainfall events are more intense, thus decay and recover more slowly The contributions from and mostly resides in the lowest 4 km. Questions 22 objectives Our interest is to look at the memory properties: the memory in the deep convection Want to see if the variability in the main state is important for the memory I want to know on what space and time the memory is important 2 Control simulation
Setup and forcing are based on the EUROCS case study Peak SHF =130 LHF= 400 Height (km) / 2 Time (hr) 3 RC is prescribed: from 0-12km, then at 15km Additional cooling at night (12-24h) at 0 km Evolution of the boundary layer Control simulation Evolution of the BL
At sunrise: the near surface is at its coolest state and the BL is stable Near surface temperature is increasing BL depth is increasing: 100m at 1h to 800m at 12h Warmest state at sunset Surface forcing is off between 12-24h Height (km) Free troposphere cools down uniformly Below 1km: the column cools more rapidly At 24h (K) Stability structure of the free troposphere is maintained close to that at 0h The BL is stable Convection depends on its own history?
Does sensitive to the A? Lag Time (Hrs) Lag Time (Hrs) Lag Time (Hrs) The amplitudes of decrease with increasing A Growing stage: No convective memory when Mature stage: Very similar memory functions for A significant reduction when and No convective memory when P
10 Steady stage shows different sensitivity for Rainfall events are a little bit enhanced and There is convective memory even within area Convection depends on its own history? Does sensitive to the A? The amplitudes of decrease with increasing A Growing stage: No convective memory when Mature stage: Very similar memory functions for A significant reduction when and No convective memory when
Steady stage shows different sensitivity for Rainfall events are a little bit enhanced and There is convective memory even within area Analysis of reveals to evaluate current convection using information from previous behaviour of convection we needs to know the size of the area within which convection is evaluated and its life cycle are Hours Lag Time (Hrs) P 10 Convection depends on its own history?
Does sensitive to the strength of surface forcing? Lag Time (Hrs) Lag Time (Hrs) shows a weak sensitivity to the strength of surface forcing Weak: rainfall events decay less rapidly and are suppressed more strongly 10 Lag Time (Hrs)
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