Biases in the albedo sensitivity to deforestation in CMIP5 models and their impacts on the associated historical Radiative Forcing

Abstract

Abstract. Climate model biases in the representation of albedo variations between land cover types contribute to uncertainties on the climate impact of land cover changes since pre-industrial times, and especially on the associated Radiative Forcing. The recent publications of new observation-based datasets offer opportunities to investigate these biases and their impact on historical 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 types, as well as the limited number of simulations isolating the land use forcing in CMIP. 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 13 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, crops/grasses, and the albedo change due to the transition between these two land cover types in the analysed models. Additionally, we reconstruct the local albedo changes induced by historical conversions between trees and crops/grasses for 15 CMIP5 models. This allows us to derive estimates of the Radiative Forcing from land cover changes since pre-industrial times ranging between 0 and −0.22 W/m2, with a mean value of −0.07 W/m2. Constraining the albedo response to transitions between trees and crops/grasses from the models with satellite-derived data leads to an increase in this range, however after excluding two models with unrealistic conversion rates from trees to crops/grasses we obtain a revised model mean estimate of −0.11 W/m2 (with individual model results between −0.04 and −0.16 W/m2). These numbers are at the lower end of the range provided by the IPCC AR5 (−0.15 ± 0.10 W/m2). The approach described in this study can be applied on other model simulations, such as those from CMIP6, especially as a diagnostic enabling the reproduction of the model evaluation part has been included in the ESMValTool v2.0.

Effective radiative forcing and adjustments in CMIP6 models

“…This experiment was partly motivated by a large land use forcing of −0.4 W m −2 in the CMIP5 HadGEM2-ES model (Andrews et al, 2017), which showed a large change in regional dust loading that contributed to this forcing. Our multi-model mean ERF of −0.09 W m −2 (−0.12 W m −2 if NorESM2-LM is excluded) agrees well with an observational-constrained analysis from CMIP5 models of −0.11 W m −2 (Lejeune et al, 2020) and is within the likely range of the AR5 assessment of −0.15 (−0.05 to −0.25) W m −2 .…”

Earth System Model Evaluation Tool (ESMValTool) v2.0 – an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP

“…In particular, historical deforestation since the pre-industrial era has led to an increase in surface albedo corresponding to a global radiative forcing of −0.15 ± 0.10 W m −2 (Myhre et al, 2013). There are however large uncertainties, even concerning the sign of the effect, regarding the impacts of LCC on near-surface temperature due to persistent model disagreement (Davin et al, 2020;de Noblet-Ducoudré et al, 2012;Lejeune et al, 2017;Pitman et al, 2009). These disagreements arise from uncertainties in (1) the interplay between radiative (albedo) and non-radiative processes (surface roughness and evaporative fraction), (2) the role of local versus large-scale processes and feedbacks (Winckler et al, 2017), and (3) the magnitude of change in given surface properties (e.g.…”

Satellites reveal recent increases in global land surface albedo that moderates global warming

“…Overall, prior researched have focused on quantifying changes in carbon processes [7,22,27,41], or the short-term albedo change caused by snow melting or anthropogenic intervention like deforestation, afforestation, expansion of urban and cropland [18][19][20][21][22][23]. Our study reveals that the global land surface albedo has been steadily rising due to the long-term changes of vegetation and surface moisture.…”

“…In general, changes in surface albedo are associated with LULC conversions, LULC non-conversions and snow/ice melting. Previous research has mainly evaluated how changes in specific LULC types or type-to-type conversions caused by human activities have affected albedo and the associated radiative forcing [18][19][20][21][22][23]. These studies, failing to explore the influences of all possible LULC conversions and snow dynamics on surface albedo, can only provide a partial quantification of the effective changes in surface albedo.…”