Arctic Radiative Fluxes: Present-Day Biases and Future Projections in CMIP5 Models

Abstract

Radiative fluxes are critical for understanding the energy budget of the Arctic region, where the climate has been changing rapidly and is projected to continue to change. This work investigates causes of present-day biases and future projections of top-of-atmosphere (TOA) Arctic radiative fluxes in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Compared to Clouds and the Earth's Radiant Energy System Energy Balanced and Filled (CERES-EBAF), CMIP5 net TOA downward shortwave (SW) flux biases are larger than outgoing longwave radiation (OLR) biases. The primary contributions to modeled TOA SW flux biases are biases in cloud amount and snow cover extent compared to the GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP) and the newly developed Making Earth System Data Records for Use in Research Environments (MEaSUREs) dataset, respectively (with most models predicting insufficient cloud amount and snow cover in the Arctic), and biases with sea ice albedo. Future projections (2081-90) with representative concentration pathway 8.5 (RCP8.5) simulations suggest increasing net TOA downward SW fluxes (18 W m 22 ) over the Arctic basin due to a decrease of surface albedo from melting snow and ice, and increasing OLR (16 W m 22 ) due to an increase in surface temperatures. The largest contribution to future Arctic net TOA downward SW flux increases is declining sea ice area, followed by declining snow cover area on land, reductions to sea ice albedo, and reductions to snow albedo on land. Cloud amount is not projected to change significantly. These results suggest the importance of accurately representing both the surface area and albedos of sea ice and snow cover as well as cloud amount in order to accurately represent TOA radiative fluxes for the present-day climate and future projections.

Improving satellite-retrieved surface radiative fluxes in polar regions using a smart sampling approach

“…van den Broeke, 2004;Sedlar et al, 2011;Gorodetskaya et al, 2015) and evaluation purposes of climate models (e.g. Gallée and Gorodetskaya, 2010;King et al, 2015;English et al, 2015). The methodology developed here can significantly increase the number of satellite-based retrievals of SW↓ and LW↓ radiation on a monthly basis, or even at finer temporal resolutions as shown in Fig.…”

Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

“…From a perspective from the top of the atmosphere to the surface, researchers have attributed the bias of the surface radiation budget to the poor ability of models to properly represent both the cloud radiation effect and the stable boundary layer (Wyser et al, 2007;Vihma and Pirazzini, 2005). The Arctic cloud, particularly the mixed-phase low cloud, is misrepresented in current state-of-the-art climate models (Pithan et al, 2013;English et al, 2015). Simulating the stable boundary layer is limited not only by the relatively coarse resolution (Steeneveld et al, 2006), but also by the lack of realistic representations of small-scale physical processes, such as turbulent mixing and snow-surface coupling (Sterk et al, 2013).…”

Understanding the predictability of the Arctic climate

“…To understand Arctic warming and its huge seasonal cycle, as well as related processes (such as sea ice decline and precipitation increases [Bintanja and Selten, 2014]) it is therefore imperative to study not only the governing feedbacks but also assess the impact of seasonally-varying radiative forcing. This also implies that an accurate representation of changes in the seasonality of radiative forcing (which are dominated by clouds [English et al, 2015], see Methods) is crucial to correctly project both the magnitude and the pattern of future Arctic warming. Uncertainties in Arctic radiative fluxes (which peak in summer [English et al, 2015]) mainly affect Arctic winter and annual warming (Holland et al, 2003), meaning that not only uncertain Arctic feedbacks but also the (spring and summer) radiative forcing contribute to the intermodel spread in Arctic climate change (Collins et al, 2013).…”

“…This also implies that an accurate representation of changes in the seasonality of radiative forcing (which are dominated by clouds [English et al, 2015], see Methods) is crucial to correctly project both the magnitude and the pattern of future Arctic warming. Uncertainties in Arctic radiative fluxes (which peak in summer [English et al, 2015]) mainly affect Arctic winter and annual warming (Holland et al, 2003), meaning that not only uncertain Arctic feedbacks but also the (spring and summer) radiative forcing contribute to the intermodel spread in Arctic climate change (Collins et al, 2013).…”

“…Simulating Arctic warming using climate models (Hazeleger et al, 2010;Holland and Bitz, 2003) involves many uncertainties though, as indicated by the large intermodel differences. This may be attributed to uncertainties in the magnitude of climate feedbacks, to intermodel differences in the representation of important physics such as radiation (English et al, 2015), and to uncertainties in the radiative forcing. In any case, the interrelation between Arctic warming and sea ice decline clearly is a vital issue (Screen and Simmons, 2010), since this connection involves many of the relevant regional Arctic feedbacks as well as the shortwave and longwave radiation characteristics that govern the Arctic climate response.…”