Abstract
Abstract. Climate model biases in the representation of albedo variations between land cover classes contribute to uncertainties on the climate impact of land cover changes since pre-industrial times, especially on the associated radiative forcing. Recent publications of new observation-based datasets offer opportunities to investigate these biases and their impact on historical surface albedo changes in simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Conducting such an assessment is, however, complicated by the non-availability of albedo values for specific land cover classes in CMIP and the limited number of simulations isolating the land use forcing. In this study, we demonstrate the suitability of a new methodology to extract the albedo of trees and crops–grasses in standard climate model simulations. We then apply it to historical runs from 17 CMIP5 models and compare the obtained results to satellite-derived reference data. This allows us to identify substantial biases in the representation of the albedo of trees and crops–grasses as well as the surface albedo change due to the transition between these two land cover classes in the analysed models. Additionally, we reconstruct the local surface albedo changes induced by historical conversions between trees and crops–grasses for 15 CMIP5 models. This allows us to derive estimates of the albedo-induced radiative forcing from land cover changes since pre-industrial times. We find a multi-model range from 0 to −0.17 W m−2, with a mean value of −0.07 W m−2. Constraining the surface albedo response to transitions between trees and crops–grasses from the models with satellite-derived data leads to a revised multi-model mean estimate of −0.09 W m−2 but an increase in the multi-model range. However, after excluding one model with unrealistic conversion rates from trees to crops–grasses the remaining individual model results vary between −0.03 and −0.11 W m−2. These numbers are at the lower end of the range provided by the IPCC AR5 (-0.15±0.10 W m−2). The approach described in this study can be applied to other model simulations, such as those from CMIP6, especially as the evaluation diagnostic described here has been included in the ESMValTool v2.0.
Highlights
The landscape transformations imposed by anthropogenic activities have the potential to modify the climate (Foley et al, 2005; Mahmood et al, 2014)
Myhre et al (2005) and Kvalevåg et al (2010) have argued that climate models usually overestimate the albedo difference between natural vegetation and croplands in comparison to satellite-derived observational evidence. This is consistent with the weaker radiative forcing of −0.09 W m−2 due to anthropogenic land cover change found by Myhre et al (2005), after combining a radiative transfer model with reconstitutions of past surface albedo changes based on satellite observations of the current vegetation land cover and its albedo, as well as a dataset for potential natural vegetation
The analyses described in this study rely on both climate model runs and satellite-based observational datasets to pursue two main objectives: (1) the validation of a methodology to systematically evaluate the representation of the surface albedo difference resulting from conversions between the dominant land cover classes in climate models in standard climate model runs, with the view to being integrated in the ESMValTool v2.0 (Eyring et al, 2020), and (2) the assessment of the radiative forcing from historical land cover changes (LCCs) using historical CMIP5 model simulations and observations to constrain model biases
Summary
The landscape transformations imposed by anthropogenic activities have the potential to modify the climate (Foley et al, 2005; Mahmood et al, 2014). Concerning the surface albedo model studies concluded that historical LCCs have led to large-scale increases in this variable (Betts et al, 2007; Boisier et al, 2013) because trees have a lower albedo than shorter vegetation types, especially in the presence of snow (Cescatti et al, 2012; Li et al, 2015) This has resulted in a cooling effect, and climate models have simulated an associated global radiative forcing (RF) close to −0.2 W m−2 (Betts et al, 2007; Davin et al, 2007; Pongratz et al, 2009). The diversity of model parameterisations was estimated to be responsible for about half of it, while the remaining uncertainties result from differences in the magnitude of the prescribed land cover
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