Cluster‐Based Evaluation of Model Compensating Errors: A Case Study of Cloud Radiative Effect in the Southern Ocean

Abstract

Model evaluation is difficult and generally relies on analysis that can mask compensating errors. This paper defines new metrics, using clusters generated from a machine learning algorithm, to estimate mean and compensating errors in different model runs. As a test case, we investigate the Southern Ocean shortwave radiative bias using clusters derived by applying self‐organizing maps to satellite data. In particular, the effects of changing cloud phase parameterizations in the MetOffice Unified Model are examined. Differences in cluster properties show that the regional radiative biases are substantially different than the global bias, with two distinct regions identified within the Southern Ocean, each with a different signed bias. Changing cloud phase parameterizations can reduce errors at higher latitudes but increase errors at lower latitudes of the Southern Ocean. Ranking the parameterizations often shows a contrast in mean and compensating errors, notably in all cases large compensating errors remain.

Constraining the Surface Flux of Sea Spray Particles From the Southern Ocean

“…As our own observational record showed, SSPs were strongly scavenged by boundary layer cloud (e.g., fog), particularly through 15–17 February and 4–6 March 2018. It is useful, however, to recall that clouds over the Southern Ocean are not very well represented within modern atmospheric models (Schuddeboom et al, ; Trenberth & Fasullo, ). Current era atmospheric models systematically underpredict the amount of low‐lying cloud and fog relative to the true cloud observed over the Southern Ocean (Kuma et al, ).…”

The sensitivity of Southern Ocean aerosols and cloud microphysics to sea spray and sulfate aerosol production in the HadGEM3-GA7.1 chemistry-climate model

“…To understand potential connections between the representation of aerosols and clouds via the aerosol indirect effect, we investigate the representation of marine aerosols over the Southern Ocean in the Hadley Centre Global Environmental Model version 3, Global Atmosphere 7.1 (HadGEM3-GA7.1). An evaluation of cloud representation in the predecessor model HadGEM3-GA7.0 suggests that significant errors exist in the cloud scheme over the Southern Ocean, but they partially compensate one another (Schuddeboom et al, 2019). Furthermore, the aerosol forcing and climate feedback in this model is highly sensitive to the representation of DMS-derived sulfate aerosol (Bodas-Salcedo et al, 2019).…”

Southern Ocean Cloud and Aerosol data: a compilation of measurements from the 2018 Southern Ocean Ross Sea Marine Ecosystems and Environment voyage

“…This leads to excessive sunlight being absorbed by the ocean (Trenberth and Fasullo, 2010;Kay et al, 2016;Hyder et al, 2018) and subsequent higher sea surface temperatures than observed (Bodas-Salcedo et al, 2012;Mechoso et al, 2016). Previous studies have also shown the importance of accurate mixed-phase cloud parameterisations over the Southern Ocean in climate models to properly simulate cloud radiative properties over the Southern Ocean (Lawson and Gettelman, 2014;Kay et al, 2016;Schuddeboom et al, 2019;Noh et al, 2019). In mixed-phase clouds, both liquid droplets and ice crystals coexist with the liquid water often being supercooled.…”

“…For example, Hyder et al (2018) recently identified that 70 % of the sea surface temperature biases observed in model simulations, performed in support of the Coupled Model Intercomparison Project 5 (CMIP5), can be attributed to the models not representing clouds and their properties correctly. These errors occur because climate models simulate too little cloud cover and contain biases in cloud albedo over the Southern Ocean (Bodas-Salcedo et al, 2012;Schuddeboom et al, 2019), resulting in projections that underestimate the reflected solar radiation at the top of the atmosphere (TOA; Haynes et al, 2011) and overestimate 2 https://doi.org/10.5194/essd-2020-321…”