Polar clouds and radiation in satellite observations, reanalyses, and climate models

Abstract

Clouds play a pivotal role in the surface energy budget of the polar regions. Here we use two largely independent data sets of cloud and surface downwelling radiation observations derived by satellite remote sensing (2007–2010) to evaluate simulated clouds and radiation over both polar ice sheets and oceans in state‐of‐the‐art atmospheric reanalyses (ERA‐Interim and Modern Era Retrospective‐Analysis for Research and Applications‐2) and the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model ensemble. First, we show that, compared to Clouds and the Earth's Radiant Energy System‐Energy Balanced and Filled, CloudSat‐CALIPSO better represents cloud liquid and ice water path over high latitudes, owing to its recent explicit determination of cloud phase that will be part of its new R05 release. The reanalyses and climate models disagree widely on the amount of cloud liquid and ice in the polar regions. Compared to the observations, we find significant but inconsistent biases in the model simulations of cloud liquid and ice water, as well as in the downwelling radiation components. The CMIP5 models display a wide range of cloud characteristics of the polar regions, especially with regard to cloud liquid water, limiting the representativeness of the multimodel mean. A few CMIP5 models (CNRM, GISS, GFDL, and IPSL_CM5b) clearly outperform the others, which enhances credibility in their projected future cloud and radiation changes over high latitudes. Given the rapid changes in polar regions and global feedbacks involved, future climate model developments should target improved representation of polar clouds. To that end, remote sensing observations are crucial, in spite of large remaining observational uncertainties, which is evidenced by the substantial differences between the two data sets.

A New Regional Climate Model for POLAR‐CORDEX: Evaluation of a 30‐Year Hindcast with COSMO‐CLM2 Over Antarctica

“…Clouds have a large radiative effect on the ice sheet and significantly contribute to the amount of incoming longwave radiation (Van Tricht et al, ). Furthermore, state‐of‐the‐art climate models have substantial biases in cloud representation over polar regions (Lenaerts et al, ). During the austral winter of 2015, ceilometer observations are available at the Princess Elisabeth station.…”

Greenland Ice Sheet Surface Mass Loss: Recent Developments in Observation and Modeling

“…This implies that models that assess present-day and predict future GrIS surface melt rates must preferably explicitly calculate the individual SEB components. In turn, these models must be evaluated with as many as possible in situ SEB observations, to assess whether the partitioning of melt energy, and therefore the sensitivity of melt to changing atmospheric/surface conditions is correctly represented [62,73,75,76]. An accurate observational estimate of melt energy requires dedicated experiments or automatic weather stations (AWS) that are operated at the ice sheet surface and measure all relevant parameters (including all radiation fluxes) sufficiently accurately to close the SEB and calculate melt energy as a residual; such stations are, e.g., operated at Summit and Swiss Camp by GC-Net [20], in the PROMICE network [14], and along the K-transect in west Greenland [57,90].…”

Arctic Summer Sea Ice Melt and Related Atmospheric Conditions in Coupled Regional Climate Model Simulations and Observations

“…Among others, the partitioning between cloud liquid water and cloud ice is a critical factor for the cloud radiative effect, but still poorly understood (e.g., Kalesse et al, 2016). Due to the uncertainty in representing Arctic cloud characteristics, such as cloud cover, opacity, phase, and vertical distribution, simulated Arctic clouds and radiation have large biases (e.g., Boeke & Taylor, 2016;English et al, 2015;Lacour et al, 2018;Lenaerts et al, 2017). .…”