Goran Georgievski

We describe a new Earth system model (ESM) experiment protocol, as part of the international Tipping Points Modelling Intercomparison Project (TIPMIP) project. We propose this as a protocol for the Coupled Model Intercomparison Project 7 (CMIP7). The protocol requires ESMs to run in CO ₂ -emission mode, with atmospheric CO ₂ a predicted variable. Forcing for the protocol consists solely of a constant emission of CO ₂ , based on each model's transient climate response to cumulative emissions of carbon dioxide (TCRE) value, to give a common global mean surface warming rate of 2 °C per century. This positive emission (ramp-up) experiment is started from the pre-industrial state of a given model. When the ramp-up run first exceeds a specified level of global warming (2 and 4 °C) relative to the model's pre-industrial global mean surface air temperature (GMSAT), CO ₂ emissions are set to zero and the positive emission run is branched into a zero-emission run. The zero-emission runs continue for 300 years. 50 years into each zero-emission run, CO ₂ emissions are set to the negative of the positive emission rate and the model run until GMSAT cools below the original pre-industrial value. Additionally, when the negative emission run started from the global warming level (GWL)=4 °C first drops below GWL=2 °C, a zero-emission run is branched off this, completing the set of experiments. Using this protocol, we are able to control the rate of global warming, and potentially also the rate of cooling, across participating models. TIPMIP experiments will support a range of analyses, including; an assessment of abrupt/rapid Earth system change under net zero CO ₂ emissions at a range of global warming levels, the long-term Earth system response to net zero CO ₂ emissions at these warming levels, the response to net negative CO ₂ emissions and the efficacy of negative emissions to drive cooling, and the reversibility of Earth system change under a pathway of positive (warming), zero, and negative (cooling) CO ₂ emissions.

During land model development, simulated carbon dynamics are often benchmarked against observational data sets to evaluate model performance. Functional relationship benchmarks are the relationship between a driving variable (e.g., temperature) and a response variable (e.g., ecosystem respiration) and are a promising tool for assessing model performance by evaluating modeled sensitivities to changing environmental conditions. However, observed functional relationships can be influenced by choices made during data collection and throughout the benchmarking process, impacting the inferred skill of land models. To avoid misrepresenting a model's true performance, it is necessary to systematically evaluate best practices when constructing functional relationship benchmarks. We developed a set of guidelines for constructing functional relationship benchmarks, considering the choice of data set, number of daily observations, temporal extent, and temporal resolution across Alaska and Canada over a 20‐year period from 2001 to 2020. The temperature sensitivity of ecosystem respiration from observations, evaluated through an apparent Q ₁₀ , is highly variable both spatially and as a result of the data processing approach applied in the benchmark formation. When benchmarking 13 models from the Warming Permafrost Model Intercomparison Project (WrPMIP), the range in inferred model skill is substantially impacted by the choices applied in constructing functional relationship benchmarks. The inferred performance of a given model is most sensitive to the number of daily observations and temporal extent, followed by choice of benchmark data set and temporal averaging. Results from this analysis can guide the development of consistent and robust functional relationships for future model evaluation studies.

Within the Tipping Point Modeling Intercomparison Project (TIPMIP; www.tipmip.org), we outline a coordinated experimental protocol designed for standalone land surface models (LSMs) as well as coupled Earth System Models (ESMs). The protocol targets key questions on permafrost feedbacks, thresholds, timescales, abrupt versus gradual, and irreversible changes, and interactions with other tipping elements, aiming to quantify plausible landscape transformations, associated greenhouse gas emissions, and their impacts on global energy, carbon, and hydrological cycles, as well as large scale circulation. Building on TIPMIP-ESM, the TIPMIP-permafrost Tier 1 experiments, defined as the core, priority experiments required for MIP participation, aim to use LSMs to quantify the extent to which permafrost area, ground ice volume, and soil carbon stocks exhibit path dependent behavior and reversibility when subjected to idealized warming, stabilization, and subsequent cooling phases. This includes evaluating whether the permafrost returns to its initial state as temperatures decline, or the extent to which certain changes, such as area loss, carbon redistribution, hydrological reorganization, or ground subsidence, persist despite cooling and thus remain effectively irreversible over time scales spanning a century to a several hundred years. In addition, during the stabilization phase we will examine whether permafrost degradation continues even after warming ceases. This involves assessing time lagged thaw response, identifying critical thresholds that may trigger rapid acceleration of degradation, and determining whether internal processes, such as shifts in soil moisture, ground subsidence and thermokarst lake formation, or carbon redistribution, amplify change through self reinforcing feedbacks. The emergence of such behaviors would indicate nonlinear system dynamics and a heightened susceptibility to tipping point transitions.Additional sensitivity experiments (Tier 2), aimed at understanding and quantifying specific processes that could induce permafrost tipping, will apply idealized forcing. The focus is on altering hydrologic conditions and modifying surface properties (e.g., vegetation, albedo, thermokarst lake distribution) to explore how these factors influence the onset and dynamics of tipping behavior.Tier 3 experiments, also sensitivity oriented, investigate the coupling between permafrost dynamics and other climate tipping elements using ESMs, for example, assessing the consequences of an AMOC collapse for high latitude permafrost stability, as well as the broader Earth system impacts of abrupt permafrost tipping.

The terrestrial biosphere plays a crucial role in regulating biogeochemical cycles, storing carbon, and providing essential ecosystem services, yet the land carbon sink remains a large source of uncertainty in future climate projections. Under the combined pressures of climate change, land use, pests, and fire, major biomes such as the Amazon and boreal forests face widespread degradation and may exhibit tipping behavior. The Tipping Points Modelling Intercomparison Project (TIPMIP) brings together the modelling community to develop unified simulation protocols for Earth System Models and standalone models, including Dynamic Global Vegetation Models. In this context, TIPMIP-BIO aims at running idealized experiments of climate overshoot and stabilizations scenarios, in order to assess climatic and deforestation thresholds that could trigger nonlinear biosphere responses. The framework provides opportunities to examine key processes and feedbacks, including CO₂ fertilization and physiological responses, and to explore emerging questions such as plant trait variability and the impact of compound climate events.

Wetlands are a major natural source of atmospheric CH₄, however, accurately estimating their emissions is difficult due to the complex biogeochemical interactions and spatial heterogeneity of wetland environments. This study explores how a combination of atmospheric inverse and process-based modelling can reduce the discrepancy in Arctic wetland estimates between bottom-up and top-down approaches. We employed the Jena CarboScope global inversion system, incorporating prior wetland fluxes simulated by the JSBACH land surface model, which is part of the Max Planck Institute Earth System Model (MPI-ESM). We conducted a series of inversion experiments, each incorporating JSBACH-generated CH₄ fluxes based on different CH₄ production Q₁₀ values to test the temperature sensitivity of emissions. Additionally, we examined the impact of changing the baseline fCH₄ fraction value, which defines the fraction of anaerobically mineralized carbon converted to CH₄, while keeping all other JSBACH and inversion settings constant. Our findings show that, at a pan-Arctic scale, using a CH₄ Q₁₀ value of 1.8 produces the best agreement between the two approaches. However, no single Q₁₀ value yielded optimal agreement between the simulated fluxes and the fluxes inferred from atmospheric observations across all subregions. Instead, the best performance varied spatially, with different CH₄ production Q₁₀ values and baseline fCH₄ fraction leading to a better flux agreement in specific areas. These results highlight the importance of using regionally specific parameters to more accurately estimate wetland CH₄ emissions, and the potential of employing atmospheric inversions to guide bottom-up process models towards regionally representative parameter settings.

Wetlands are a major natural source of atmospheric CH₄, however, accurately estimating their emissions is difficult due to the complex biogeochemical interactions and spatial heterogeneity of wetland environments. This study explores how a combination of atmospheric inverse and process-based modelling can reduce the discrepancy in Arctic wetland estimates between bottom-up and top-down approaches. We employed the Jena CarboScope global inversion system, incorporating prior wetland fluxes simulated by the JSBACH land surface model, which is part of the Max Planck Institute Earth System Model (MPI-ESM). We conducted a series of inversion experiments, each incorporating JSBACH-generated CH₄ fluxes based on different CH₄ production Q₁₀ values to test the temperature sensitivity of emissions. Additionally, we examined the impact of changing the baseline fCH₄ fraction value, which defines the fraction of anaerobically mineralized carbon converted to CH₄, while keeping all other JSBACH and inversion settings constant. Our findings show that, at a pan-Arctic scale, using a CH₄ Q₁₀ value of 1.8 produces the best agreement between the two approaches. However, no single Q₁₀ value yielded optimal agreement between the simulated fluxes and the fluxes inferred from atmospheric observations across all subregions. Instead, the best performance varied spatially, with different CH₄ production Q₁₀ values and baseline fCH₄ fraction leading to a better flux agreement in specific areas. These results highlight the importance of using regionally specific parameters to more accurately estimate wetland CH₄ emissions, and the potential of employing atmospheric inversions to guide bottom-up process models towards regionally representative parameter settings.

Wetlands are a major natural source of atmospheric CH₄, however, accurately estimating their emissions is difficult due to the complex biogeochemical interactions and spatial heterogeneity of wetland environments. This study explores how a combination of atmospheric inverse and process-based modelling can reduce the discrepancy in Arctic wetland estimates between bottom-up and top-down approaches. We employed the Jena CarboScope global inversion system, incorporating prior wetland fluxes simulated by the JSBACH land surface model, which is part of the Max Planck Institute Earth System Model (MPI-ESM). We conducted a series of inversion experiments, each incorporating JSBACH-generated CH₄ fluxes based on different CH₄ production Q₁₀ values to test the temperature sensitivity of emissions. Additionally, we examined the impact of changing the baseline fCH₄ fraction value, which defines the fraction of anaerobically mineralized carbon converted to CH₄, while keeping all other JSBACH and inversion settings constant. Our findings show that, at a pan-Arctic scale, using a CH₄ Q₁₀ value of 1.8 produces the best agreement between the two approaches. However, no single Q₁₀ value yielded optimal agreement between the simulated fluxes and the fluxes inferred from atmospheric observations across all subregions. Instead, the best performance varied spatially, with different CH₄ production Q₁₀ values and baseline fCH₄ fraction leading to a better flux agreement in specific areas. These results highlight the importance of using regionally specific parameters to more accurately estimate wetland CH₄ emissions, and the potential of employing atmospheric inversions to guide bottom-up process models towards regionally representative parameter settings.

We describe a new Earth system model (ESM) experiment protocol, as part of the international Tipping Points Modelling Intercomparison Project (TIPMIP) project. We propose this as a protocol for the Coupled Model Intercomparison Project 7 (CMIP7). The protocol requires ESMs to run in CO2-emission mode, with atmospheric CO2 a predicted variable. Forcing for the protocol consists solely of a constant emission of CO2, based on each model’s transient climate response to cumulative emissions of carbon dioxide (TCRE) value, to give a common global mean warming rate of 2 °C per century. This positive emission experiment is started from the pre-industrial state of a given model. When the ramp-up run first exceeds a specified level of global warming (2 °C and 4 °C) relative to the model’s pre-industrial global mean surface air temperature (GMSAT), CO2 emissions are set to zero and the positive emission run is branched into a zero-emission run. The zero-emission runs continue for 300 years. At 50 years into each zero-emission run, CO2 emissions are set to the negative of the positive emission rate and the model run until GMSAT cools below the original pre-industrial value. Additionally, when the negative emission run started from global warming level (GWL) = 4 °C first drops below GWL = 2 °C, a zero-emission run is branched off this, completing the set of experiments. Using this protocol, we are able to control the rate of global warming and cooling across participating models. TIPMIP experiments will support a range of analyses, including; an assessment of abrupt/rapid Earth system change, the long-term response to zero CO2 emissions, the response to negative CO2 emissions, the efficacy of negative emissions in driving cooling, and the reversibility of Earth system change under a pathway of positive (warming), zero (stabilization), and negative (cooling) CO2 emissions.

Wetlands are a major natural source of atmospheric CH₄, however, accurately estimating their emissions is difficult due to the complex biogeochemical interactions and spatial heterogeneity of wetland environments. This study explores how a combination of atmospheric inverse and process-based modelling can reduce the discrepancy in Arctic wetland estimates between bottom-up and top-down approaches. We employed the Jena CarboScope global inversion system, incorporating prior wetland fluxes simulated by the JSBACH land surface model, which is part of the Max Planck Institute Earth System Model (MPI-ESM). We conducted a series of inversion experiments, each incorporating JSBACH-generated CH₄ fluxes based on different CH₄ production Q₁₀ values to test the temperature sensitivity of emissions. Additionally, we examined the impact of changing the baseline fCH₄ fraction value, which defines the fraction of anaerobically mineralized carbon converted to CH₄, while keeping all other JSBACH and inversion settings constant. Our findings show that, at a pan-Arctic scale, using a CH₄ Q₁₀ value of 1.8 produces the best agreement between the two approaches. However, no single Q₁₀ value yielded optimal agreement between the simulated fluxes and the fluxes inferred from atmospheric observations across all subregions. Instead, the best performance varied spatially, with different CH₄ production Q₁₀ values and baseline fCH₄ fraction leading to a better flux agreement in specific areas. These results highlight the importance of using regionally specific parameters to more accurately estimate wetland CH₄ emissions, and the potential of employing atmospheric inversions to guide bottom-up process models towards regionally representative parameter settings.