© Crown copyright Met Office Systematic Biases in Microphysics: observations and parametrization Ian Boutle, Steven Abel, Peter Hill, Cyril Morcrette QJ Roy Met Soc, 2013, doi: /qj.2140

© Crown copyright Met Office Table of Contents Motivation Microphysical Parametrization Quantification of sub-grid variability Consequences for microphysical process rates Effects in model simulations

© Crown copyright Met Office Currently assume that cloud is homogeneous for microphysics calculations Climate grid-box (~125km) NWP grid- box (~25km) GOES SW Satellite Image, SE Pacific during VOCALS

© Crown copyright Met Office Motivation Also assume rain is homogeneous across a grid-box Microphysical process rates are typically nonlinear, so the inhomogeneity is likely to matter (Pincus & Klein, Morrison & Gettelman, Larson & Griffin) ~36km W-band cloud radar on Ron Brown research vessel during VOCALS

© Crown copyright Met Office Microphysical Process Rates Typically these are given as non-linear power laws, taking some species (q) as input: In a GCM, we want the grid-box averaged process rate, but only have the grid-box averaged q Hence for b≠1: This introduces a systematic bias into the model

© Crown copyright Met Office Analytically correcting process rates Suppose we have some information about the sub-grid distribution of q, e.g. that it can be represented by a log- normal distribution: Then we can integrate over the PDF to obtain: We can improve our estimate of M by just knowing f and the PDF shape

© Crown copyright Met Office Parametrizing f (cloud) Continued increase at all scales Strongest at smallest scales (1/3 power law)

© Crown copyright Met Office Parametrizing f (cloud) CloudSat misses smaller, thinner clouds due to resolution and instrument sensitivity

© Crown copyright Met Office Parametrizing f (rain) More variability Similar variation with grid-box size and fraction Similar problems with CloudSat data

© Crown copyright Met Office PDF Shape Gamma or log-normal distributions both provide good descriptions of the cloud and rain variability Gamma is slightly better, but the maths is more tractable with log-normal! CloudRain

© Crown copyright Met Office Process rate biases Process rates are under-estimated by a factor of 2-4 Knowledge of cloud-rain correlation and overlap also important

© Crown copyright Met Office Modelling Consequences 1km333m100m Control simulation, microphysics the same at all resolutions New simulation, includes parametrization of sub-grid variability (& microphys change)

© Crown copyright Met Office Modelling Consequences Accumulated precipitation over 48 hours is a factor of 4 greater in 100m simulation than in 1km simulation Using parametrization of sub-grid variability corrects for this without the need for tuning 1km 333m 100mControl New

© Crown copyright Met Office Conclusions There is a lot of unresolved variability in cloud and precipitation in a GCM Radiation schemes have taken account of this for many years Microphysics schemes should do as well Simple analytical methods can be used to correct most process rates A parametrization for the variability of liquid cloud and rain has been developed based on observations from aircraft, CloudSat and CloudNet-ARM Also for ice cloud, see Hill et al. (2012, QJ)

© Crown copyright Met Office Conclusions Without parametrization of sub-grid variability, autoconversion and accretion rates are underestimated by a factor of 2-4 Model simulations show a factor of 4 increase in precipitation with a factor of 10 increase in resolution The parametrization described improves both the process rates and model simulations Still more to consider – cloud-rain correlation and overlap is important (Lebsock et al, 2013) Would like to know more about regime dependence

© Crown copyright Met Office Questions?