Abstract
Abstract. The evaluation of modelling diagnostics with appropriate observations is an important task that establishes the capabilities and reliability of models. In this study we compare aerosol and cloud properties obtained from three different climate models (ECHAM-HAM, ECHAM-HAM-SALSA, and NorESM) with satellite observations using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulator MODIS-COSP version 1.4 was implemented into the climate models to obtain MODIS-like cloud diagnostics, thus enabling model-to-model and model-to-satellite comparisons. Cloud droplet number concentrations (CDNCs) are derived identically from MODIS-COSP-simulated and MODIS-retrieved values of cloud optical depth and effective radius. For CDNC, the models capture the observed spatial distribution of higher values typically found near the coasts, downwind of the major continents, and lower values over the remote ocean and land areas. However, the COSP-simulated CDNC values are higher than those observed, whilst the direct model CDNC output is significantly lower than the MODIS-COSP diagnostics. NorESM produces large spatial biases for ice cloud properties and thick clouds over land. Despite having identical cloud modules, ECHAM-HAM and ECHAM-HAM-SALSA diverge in their representation of spatial and vertical distributions of clouds. From the spatial distributions of aerosol optical depth (AOD) and aerosol index (AI), we find that NorESM shows large biases for AOD over bright land surfaces, while discrepancies between ECHAM-HAM and ECHAM-HAM-SALSA can be observed mainly over oceans. Overall, the AIs from the different models are in good agreement globally, with higher negative biases in the Northern Hemisphere. We evaluate the aerosol–cloud interactions by computing the sensitivity parameter ACICDNC=dln(CDNC)/dln(AI) on a global scale. However, 1 year of data may be considered not enough to assess the similarity or dissimilarities of the models due to large temporal variability in cloud properties. This study shows how simulators facilitate the evaluation of cloud properties and expose model deficiencies, which are necessary steps to further improve the parameterisation in climate models.
Highlights
A climate model is a powerful tool for investigating the response of the climate system to various forcings, enabling climate forecasts on seasonal to decadal timescales, and it can be used for estimating projections of the future climate over the coming centuries based on future greenhouse gas and aerosol forcing scenarios (Flato, 2011)
From the spatial distributions of aerosol optical depth (AOD) and aerosol index (AI), we find that Norwegian Earth System Model (NorESM) shows large biases for AOD over bright land surfaces, while discrepancies between ECHAM-HAM and ECHAM-HAM-SALSA can be observed mainly over oceans
We compare on a global scale aerosol and cloud properties from the three models by subtracting Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals from the modelled COSP diagnostics
Summary
A climate model is a powerful tool for investigating the response of the climate system to various forcings, enabling climate forecasts on seasonal to decadal timescales, and it can be used for estimating projections of the future climate over the coming centuries based on future greenhouse gas and aerosol forcing scenarios (Flato, 2011). G. Saponaro et al.: Evaluating cloud diagnostics in ECHAM-HAM, ECHAM-HAM-SALSA, and NorESM. The evaluation of modelling diagnostics is an important task that establishes the capabilities and reliability of models. When key properties of the atmosphere (e.g. clouds, aerosols) are considered, the model assessment is relevant to ensure that the climate model correctly captures key features of the climate system. The interest in the reliability of climate models reaches outside the scientific community, as these simulations will form the basis for future climate assessments and negotiations. Understanding the level of reliability is a necessary step to strengthen the robustness of climate projections and, if necessary, improve the model parameterisations for the relevant processes
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
