The Cloud Feedback Model Intercomparison Project (CFMIP) Diagnostic Codes Catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models

Abstract

Abstract. The CFMIP Diagnostic Codes Catalogue assembles cloud metrics, diagnostics and methodologies, together with programs to diagnose them from general circulation model (GCM) outputs written by various members of the CFMIP community. This aims to facilitate use of the diagnostics by the wider community studying climate and climate change. This paper describes the diagnostics and metrics which are currently in the catalogue, together with examples of their application to model evaluation studies and a summary of some of the insights these diagnostics have provided into the main shortcomings in current GCMs. Analysis of outputs from CFMIP and CMIP6 experiments will also be facilitated by the sharing of diagnostic codes via this catalogue.Any code which implements diagnostics relevant to analysing clouds -including cloud-circulation interactions and the contribution of clouds to estimates of climate sensitivity in models -and which is documented in peer-reviewed studies, can be included in the catalogue. We very much welcome additional contributions to further support community analysis of CMIP6 outputs.

Incorporation of inline warm-rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation

“…For this study, we incorporated two such diagnostics based on the CloudSat and MODIS satellite simulators into COSP2 to evaluate cloud-to-rain microphysical transition processes represented in GCMs using satellite observations. Both diagnostics 20 are applied only to single-layer warm clouds (SLWCs) and their results are constructed with the aid of the column simulators, as illustrated in Fig. 1.…”

“…The recent and significant redesign of COSP aimed to provide more robust and efficient code (Swales et al, 2018). The updated package (COSP2) enhances the flexibility by allowing for native model subgrid cloud representations to be used 20 as input for the COSP2 interface. Using inputs from a host model, simulators in COSP2 perform two main tasks ( Fig.…”

“…This is because these processes typically occur over short timescales ("fast processes") and contribute to the regime dependency of important phenomena including aerosol-cloudprecipitation interactions (Michibata et al, 2016). This requires high-frequency data output (∼6 hourly) from COSP (see also Tsushima et al (2017)), which results in large amounts of data, particularly when subcolumn (pixel scale) variables, such as the radar or lidar simulators are involved. The recommendation to COSP users is to assume approximately 30 100 subcolumns per 1 • of model grid spacing.…”

Data Assimilation for Climate Research: Model Parameter Estimation of Large‐Scale Condensation Scheme

“…From the late 1990s to 2000s, passive sensors on polar-orbiting and geostationary satellites have been widely used to evaluate cloud properties in GCMs (e.g., Quaas & Boucher, 2005;Quaas et al, 2006;Rotstayn & Liu, 2003;Suzuki et al, 2004). This joint observation enables so-called "processoriented" validation to evaluate the growth processes of cloud particles (e.g., Tsushima et al, 2017;Thomas et al, 2019). This joint observation enables so-called "processoriented" validation to evaluate the growth processes of cloud particles (e.g., Tsushima et al, 2017;Thomas et al, 2019).…”

“…Since the launch of CloudSAT in 2006, it has been possible to assess the growth process of cloud particles, as well as the cloud properties themselves, through joint observation by the active sensor on CloudSAT and passive sensors on other satellites included in the A-train constellation (Nakajima et al, 2010a;Nakajima et al, 2010b;Suzuki et al, 2010). This joint observation enables so-called "processoriented" validation to evaluate the growth processes of cloud particles (e.g., Tsushima et al, 2017;Thomas et al, 2019).…”