Recent decline in tropical temperature sensitivity of atmospheric CO2 growth rate variability.

A two-fold enhancement in the sensitivity of atmospheric CO2 growth rate (CGR) to tropical temperature interannual variability ( Γ CGR T $$ {\varGamma}_{\mathrm{CGR}}^T $$ ) till early 2000s has been reported, which suggests a drought-induced shift in terrestrial carbon cycle responding temperature fluctuations, thereby accelerating global warming. However, using six decades long atmospheric CO2 observations, we show that Γ CGR T $$ {\varGamma}_{\mathrm{CGR}}^T $$ has significantly declined in the last two decades, to the level during the 1960s. The Γ CGR T $$ {\varGamma}_{\mathrm{CGR}}^T $$ decline begs the question of whether the sensitivity of ecosystem carbon cycle to temperature variations at local scale has largely decreased. With state-of-the-art dynamic global vegetation models, we further find that the recent Γ CGR T $$ {\varGamma}_{\mathrm{CGR}}^T $$ decline is barely attributed to ecosystem carbon cycle response to temperature fluctuations at local scale, which instead results from a decrease in spatial coherence in tropical temperature variability and land use change. Our results suggest that the recently reported loss of rainforest resilience has not shown marked influence on the temperature sensitivity of ecosystem carbon cycle. Nevertheless, the increasing extent of land use change as well as more frequent and intensive drought events are likely to modulate the responses of ecosystem carbon cycle to temperature variations in the future. Therefore, our study highlights the priority to continuously monitor the temperature sensitivity of CGR variability and improve Earth system model representation on land use change, in order to predict the carbon-climate feedback.

Resonance between projected Tibetan Plateau surface darkening and Arctic climate change.

The Tibetan Plateau (TP) exerts a profound influence on global climate over million-year timescales due to its past uplift. However, whether the ongoing climate changes over the TP, particularly the persistent reduction in its local albedo (referred to as "TP surface darkening"), can exert global impacts remains elusive. In this study, a state-of-the-art coupled land-atmosphere global climate model has been employed to scrutinize the impact of TP darkening on polar climate changes. Results indicate that the projected TP darkening has the potential to generate a stationary Rossby wave train, thereby modulating the atmospheric circulation in the high-latitudes of the Northern Hemisphere and instigating a dipole-like surface air temperature anomaly pattern around the Arctic region. An additional experiment suggests that the projected Arctic warming may in return warm the TP, thus forming a bi-directional linkage between these two climate systems. Given their association with vast ice reservoirs, the elucidation of this mechanism in our study is crucial in advancing our comprehension of Earth system climate projections.

Regional and tele-connected impacts of the Tibetan Plateau surface darkening.

Despite knowledge of the presence of the Tibetan Plateau (TP) in reorganizing large-scale atmospheric circulation, it remains unclear how surface albedo darkening over TP will impact local glaciers and remote Asian monsoon systems. Here, we use a coupled land-atmosphere global climate model and a glacier model to address these questions. Under a high-emission scenario, TP surface albedo darkening will increase local temperature by 0.24 K by the end of this century. This warming will strengthen the elevated heat pump of TP, increasing South Asian monsoon precipitation while exacerbating the current "South Flood-North Drought" pattern over East Asia. The albedo darkening-induced climate change also leads to an accompanying TP glacier volume loss of 6.9%, which further increases to 25.2% at the equilibrium, with a notable loss in western TP. Our findings emphasize the importance of land-surface change responses in projecting future water resource availability, with important implications for water management policies.

Data-driven estimates of global litter production imply slower vegetation carbon turnover.

Accurate quantification of vegetation carbon turnover time (τveg ) is critical for reducing uncertainties in terrestrial vegetation response to future climate change. However, in the absence of global information of litter production, τveg could only be estimated based on net primary productivity under the steady-state assumption. Here, we applied a machine-learning approach to derive a global dataset of litter production by linking 2401 field observations and global environmental drivers. Results suggested that the observation-based estimate of global natural ecosystem litter production was 44.3 ± 0.4 Pg C year-1 . By contrast, land-surface models (LSMs) overestimated the global litter production by about 27%. With this new global litter production dataset, we estimated global τveg (mean value 10.3 ± 1.4 years) and its spatial distribution. Compared to our observation-based τveg , modelled τveg tended to underestimate τveg at high latitudes. Our empirically derived gridded datasets of litter production and τveg will help constrain global vegetation models and improve the prediction of global carbon cycle.