Effects of Increased Drought in Amazon Forests Under Climate Change: Separating the Roles of Canopy Responses and Soil Moisture

The Amazon forests are one of the largest ecosystem carbon pools on Earth. Although more frequent and prolonged future droughts have been predicted, the impacts have remained largely uncertain, as most land surface models (LSMs) fail to capture the vegetation drought responses. In this study, the ability of the LSM JSBACH to simulate the drought responses of leaf area index (LAI) and leaf litter production in the Amazon forests is evaluated against artificial drought experiments. Based on the evaluation, improvements are implemented, including a dependency of leaf growth on leaf carbon allocation and a better representation of drought-dependent leaf shedding. The modified JSBACH is shown to capture the drought responses at two sites and across different regions of the basin. It is then coupled with an atmospheric model to simulate the carbon and biogeophysical feedbacks of drought under future climate. We separate the drought impacts into (a) the direct effect, resulting from drier soil and stomatal closure, which does not involve a change in canopy structure, and (b) the LAI effect, resulting from leaf shedding and involving canopy response. We show that the latter accounts for 35% of reduced land carbon uptake (9 ± 10 vs. 26 ± 7 g/m2/yr; mean ± 1 sd) and 12% of surface warming (0.09 ± 0.03 vs. 0.7 ± 0.07 K) during the late 21st century. A north-south dipole of precipitation change is found, which is largely attributable to the direct effect. The results highlight the importance of incorporating drought deciduousness of tropical rainforests in LSMs to better simulate land-atmosphere interactions in the future

Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP6 models

The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP6 are of interest. Here, we analyze 32 models of the latest CMIP6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with a high agreement between the models independent of the SSP if global warming is the dominant forcing of the monsoon dynamics as it is in the 21st century; the multi-model mean for JJAS projects an increase of 0.33 mm d−1 and 5.3 % per kelvin of global warming. This is significantly higher than in the CMIP5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP6 simulations largely confirm the findings from CMIP5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall

Detectability of Artificial Ocean Alkalinization and Stratospheric Aerosol Injection in MPI‐ESM

To monitor the success of carbon dioxide removal (CDR) or solar radiation management (SRM) that offset anthropogenic climate change, the forced response to any external forcing is required to be detectable against internal variability. Thus far, only the detectability of SRM has been examined using both a stationary and nonstationary detection and attribution method. Here, the spatiotemporal detectability of the forced response to artificial ocean alkalinization (AOA) and stratospheric aerosol injection (SAI) as exemplary methods for CDR and SRM, respectively, is compared in Max Planck Institute Earth System Model (MPI-ESM) experiments using regularized optimal fingerprinting and single-model estimates of internal variability, while working under a stationary or nonstationary null hypothesis. Although both experiments are forced by emissions according to the Representative Concentration Pathway 8.5 (RCP8.5) and target the climate of the RCP4.5 scenario using AOA or SAI, detection timescales reflect the fundamentally different forcing agents. Moreover, detectability timescales are sensitive to the choice of null hypothesis. Globally, changes in the CO2 system in seawater are detected earlier than the response in temperature to AOA but later in the case of SAI. Locally, the detection time scales depend on the physical, chemical, and radiative impacts of CDR and SRM forcing on the climate system, as well as patterns of internal variability, which is highlighted for oceanic heat and carbon storage

Land Use Effects on Climate: Current State, Recent Progress, and Emerging Topics

As demand for food and fiber, but also for negative emissions, brings most of the Earth’s land surface under management, we aim to consolidate the scientific progress of recent years on the climatic effects of global land use change, including land management, and related land cover changes (LULCC)

Simulating growth-based harvest adaptive to future climate change

Forests are the main source of biomass production from solar energy and take up around 2.4 +/- 0.4 PgC per year globally. Future changes in climate may affect forest growth and productivity. Currently, state-of-the-art Earth system models use prescribed wood harvest rates in future climate projections. These rates are defined by integrated assessment models (IAMs), only accounting for regional wood demand and largely ignoring the supply side from forests. Therefore, we assess how global growth and harvest potentials of forests change when they are allowed to respond to changes in environmental conditions. For this, we simulate wood harvest rates oriented towards the actual rate of forest growth. Applying this growth-based harvest rule (GB) in JSBACH, the land component of the Max Planck Institute's Earth system model, forced by several future climate scenarios, we realized a growth potential 2 to 4 times (3-9 PgC yr(-1)) the harvest rates prescribed by IAMs (1-3 PgC yr(-1)). Limiting GB to managed forest areas (MF), we simulated a harvest potential of 3-7 PgC yr(-1), 2 to 3 times higher than IAMs. This highlights the need to account for the dependence of forest growth on climate. To account for the long-term effects of wood harvest as integrated in IAMs, we added a life cycle analysis, showing that the higher supply with MF as an adaptive forest harvesting rule may improve the net mitigation effects of forest harvest during the 21st century by sequestering carbon in anthropogenic wood products

Tracking 21st century anthropogenic and natural carbon fluxes through model-data integration

Monitoring the implementation of emission commitments under the Paris agreement relies on accurate estimates of terrestrial carbon fluxes. Here, we assimilate a 21st century observation-based time series of woody vegetation carbon densities into a bookkeeping model (BKM). This approach allows us to disentangle the observation-based carbon fluxes by terrestrial woody vegetation into anthropogenic and environmental contributions. Estimated emissions (from land-use and land cover changes) between 2000 and 2019 amount to 1.4 PgC yr −1 , reducing the difference to other carbon cycle model estimates by up to 88% compared to previous estimates with the BKM (without the data assimilation). Our estimates suggest that the global woody vegetation carbon sink due to environmental processes (1.5 PgC yr −1 ) is weaker and more susceptible to interannual variations and extreme events than estimated by state-of-the-art process-based carbon cycle models. These findings highlight the need to advance model-data integration to improve estimates of the terrestrial carbon cycle under the Global Stocktake