David M. Lawrence

We examine the behavior of ten Earth system models under a set of idealized CO2 emissions-driven simulations to look at regional temperature and precipitation changes in response to either an abrupt cessation of emissions or an idealized exceedance-and-return pathway, in which emissions linearly ramp down from positive to negative emissions. This allows a comparison of the regional climate at three different time points, all of which have the same level of cumulative CO2 emissions and therefore the same expected global warming amount. I.e., this lets us ask how regional climates would differ if we first emit, and then remove, a given amount of CO2, as compared to a case where the CO2 past a certain amount hadn't been emitted, and the coupled carbon-climate system had been left to freely evolve over the same length of time. We find consistently higher global precipitation for a given global temperature after zero emissions in both pathways, with a larger increase under the exceedance-and-return pathway than the zero-emissions pathway. Averaging over land and ocean domains, we find cooling over land relative to ocean, which is also stronger under exceedance-and-return than zero-emissions, and for about half of the models a difference in the relationship between land-ocean temperature contrast and global mean temperature change than occurs under positive emissions. We find low agreement across different models in the regional patterns of both temperature and precipitation change in all scenarios. For most of the models, there is a strong gridcell-level correlation between the temperature changes in the exceedance-and-return and zero-emissions responses, such that roughly half of the regional variance in temperature change at a given location that occurs under the exceedance-and-return pathway also occurs under zero emissions. Because only a single realisation of each experiment is available, this shared fraction is a lower bound on the common forced response: internal variability, which is uncorrelated between the two experiments, acts to reduce the apparent correspondence rather than inflate it. However, for some models there is low or negative correlation between the regional climate changes in the two cases, indicating a potential for fundamentally different regional climate change under exceedance-and-return than under zero emissions. Regional precipitation changes are more different between the exceedance-and-return and zero-emissions responses for all models.

The process of developing Earth System Models (ESMs) varies across modeling centers globally, but the overarching goal is largely the same – to improve the representation of physically-based processes such that biases in the mean climate state and its variability are minimized. Developers face a number of scientific and technical hurdles to ensure the best use of limited computational resources, storage space, and time. These challenges are particularly pronounced for modes of climate variability that are characterized by high internal variability, like the El Niño Southern Oscillation (ENSO). In this study, we leverage the development of the Community Earth System Model version 3 (CESM3) as a case study to illustrate some of these difficulties as well as to highlight new model developments and their impacts on ENSO. ENSO is a complicated indicator of model performance in that it can be characterized by a wide number of metrics to assess how well (or poorly) it is simulated. This is at odds with the model development process as a whole, which requires that a manageably small number of metrics be selected for model analysis as teams conduct hundreds of simulations to arrive at a single "best" model configuration. Selecting too many metrics runs the risk of dramatically slowing progress in developing a coupled ESM. As a result, we discuss a minimal set of ENSO metrics here that we consider indicative of overall model performance. We find that biases in the spatial extent of ENSO events persisted throughout the development cycle, with sea surface temperature anomalies (SSTa) that extend too far into the West Pacific for all simulations. Other metrics are more sensitive to model changes, including ENSO amplitude and duration. Such metrics are, however, prone to significant internal variability. This is confirmed by temporally sub-sampling long pre-industrial control simulations of previous model versions (CESM1 and CESM2), which also adds critical context to the evaluation of CESM3; any new model version should ideally not be markedly worse than past iterations. Ultimately, CESM3 produces a reasonable ENSO in comparison to previous model versions and relative to observations, but it remains difficult to attribute changes in its representation to individual model changes due to the significant internal variability.

The process of developing Earth System Models (ESMs) varies across modeling centers globally, but the overarching goal is largely the same – to improve the representation of physically-based processes such that biases in the mean climate state and its variability are minimized. Developers face a number of scientific and technical hurdles to ensure the best use of limited computational resources, storage space, and time. These challenges are particularly pronounced for modes of climate variability that are characterized by high internal variability, like the El Niño Southern Oscillation (ENSO). In this study, we leverage the development of the Community Earth System Model version 3 (CESM3) as a case study to illustrate some of these difficulties as well as to highlight new model developments and their impacts on ENSO. ENSO is a complicated indicator of model performance in that it can be characterized by a wide number of metrics to assess how well (or poorly) it is simulated. This is at odds with the model development process as a whole, which requires that a manageably small number of metrics be selected for model analysis as teams conduct hundreds of simulations to arrive at a single "best" model configuration. Selecting too many metrics runs the risk of dramatically slowing progress in developing a coupled ESM. As a result, we discuss a minimal set of ENSO metrics here that we consider indicative of overall model performance. We find that biases in the spatial extent of ENSO events persisted throughout the development cycle, with sea surface temperature anomalies (SSTa) that extend too far into the West Pacific for all simulations. Other metrics are more sensitive to model changes, including ENSO amplitude and duration. Such metrics are, however, prone to significant internal variability. This is confirmed by temporally sub-sampling long pre-industrial control simulations of previous model versions (CESM1 and CESM2), which also adds critical context to the evaluation of CESM3; any new model version should ideally not be markedly worse than past iterations. Ultimately, CESM3 produces a reasonable ENSO in comparison to previous model versions and relative to observations, but it remains difficult to attribute changes in its representation to individual model changes due to the significant internal variability.

This paper is motivated by growing concern about tipping dynamics and processes that can cause abrupt, self-amplifying, and irreversible changes across the Earth system and societies. It assesses current scientific understanding of the most severe climate outcomes, and proposes ways to address the deep uncertainty surrounding them. We find substantial, though difficult-to-quantify, probabilities of major disruptions to large-scale ocean circulation, including the Atlantic Meridional Overturning Circulation and the Southern Ocean Abyssal Circulation, with the potential to cause global climate shifts. The probability of ice sheet instability in vulnerable sectors, particularly Antarctica's marine-based sectors, is already high and could accelerate sea level rise over centennial to multi-centennial timescales. Permafrost thaw is underway, with the potential for accelerated greenhouse gas release. Tropical forests face increasing risks of widespread tree mortality, with major consequences for biodiversity, regional climate, and water availability. Coral reef dieback and mountain glacier loss are also underway, while severe compound marine events may trigger irreversible ecosystem changes. Even gradual climate change can produce nonlinear, abrupt impacts when hazards breach physiological or socioecological thresholds, disrupting health, food, or economic systems. It is essential to determine how the probability of such outcomes depends on the level and rate of warming. Current modelling systems challenge our ability to reliably quantify these risks. Sustained investment in observations, process understanding and next-generation earth system modelling is critical to identify precursors and improve early warning. Decision frameworks are needed to support proactive, flexible, anticipatory adaptation. Effective mitigation reduces risks of these highly disruptive outcomes.

Land carbon sinks are responsible for removing about a quarter of anthropogenic CO ₂ emissions, and make up approximately half of total global carbon sinks. Uncertainty in the response of land carbon sinks to climate and changing atmospheric CO ₂ are large, and dominate the uncertainty in total carbon sinks under future climate. Understanding the carbon cycle response to net-zero and net-negative emissions has important implications for projecting future climate. Here we characterize the response of land carbon pools and fluxes from ten emissions-driven Earth system models (ESMs) under positive, net-zero, and net-negative CO ₂ emissions using experiments from the “flat10” model intercomparison. Although there are many differences in simulated land carbon pools and fluxes across models, we find some consistent behavior across ESMs. (1) During the positive emissions phase, carbon is gained on land primarily in vegetation pools. (2) Following net-negative emissions to the point of cumulative zero emissions, carbon is lost from land in tropical latitudes, primarily from vegetation pools, but in mid- and high-latitudes most models show net land carbon gain, primarily in soil pools. (3) Following an extended period of net-zero emissions, a majority of models again show carbon gain in mid- and high-latitudes and vegetation carbon loss in the tropics. Under net-negative emissions the timing of vegetation carbon response relative to peak emissions is relatively consistent across ESMs, but timing of soil carbon response varies widely, implying larger intermodel disagreement associated with responses of soil carbon which tends to have longer timescales relative to vegetation carbon. Our findings highlight that tropical carbon is most likely to be both gained and subsequently lost under positive, zero, declining, and negative emissions, with possible implications for carbon dioxide removal efforts.

The process of developing Earth System Models (ESMs) varies across modeling centers globally, but the overarching goal is largely the same – to improve the representation of physically-based processes such that biases in the mean climate state and its variability are minimized. Developers face a number of scientific and technical hurdles to ensure the best use of limited computational resources, storage space, and time. These challenges are particularly pronounced for modes of climate variability that are characterized by high internal variability, like the El Niño Southern Oscillation (ENSO). In this study, we leverage the development of the Community Earth System Model version 3 (CESM3) as a case study to illustrate some of these difficulties as well as to highlight new model developments and their impacts on ENSO. ENSO is a complicated indicator of model performance in that it can be characterized by a wide number of metrics to assess how well (or poorly) it is simulated. This is at odds with the model development process as a whole, which requires that a manageably small number of metrics be selected for model analysis as teams conduct hundreds of simulations to arrive at a single "best" model configuration. Selecting too many metrics runs the risk of dramatically slowing progress in developing a coupled ESM. As a result, we discuss a minimal set of ENSO metrics here that we consider indicative of overall model performance. We find that biases in the spatial extent of ENSO events persisted throughout the development cycle, with sea surface temperature anomalies (SSTa) that extend too far into the West Pacific for all simulations. Other metrics are more sensitive to model changes, including ENSO amplitude and duration. Such metrics are, however, prone to significant internal variability. This is confirmed by temporally sub-sampling long pre-industrial control simulations of previous model versions (CESM1 and CESM2), which also adds critical context to the evaluation of CESM3; any new model version should ideally not be markedly worse than past iterations. Ultimately, CESM3 produces a reasonable ENSO in comparison to previous model versions and relative to observations, but it remains difficult to attribute changes in its representation to individual model changes due to the significant internal variability.

Agriculture and urbanization have profoundly altered the terrestrial nitrogen cycle, leading to widespread water quality degradation. Nitrogen leached from managed soils is transported and transformed through river networks, influencing downstream nutrient dynamics and ecosystem functioning. Limited knowledge of riverine nitrogen transport highlights the need for improved modeling of nitrogen transport and transformation in land models. This study presents first‐order estimates of large‐scale stream nitrate transport and reaction by using a newly implemented nitrogen routing scheme in the Model for Scale Adaptive River Transport (MOSART), the river routing scheme in the Community Land Model version 5 (CLM5). The new scheme explicitly simulates nitrate‐nitrogen concentrations in rivers by resolving the one‐dimensional advection‐dispersion‐reaction equation. The model is tested over the Great Lakes Basin (GLB) with fertilizer applications prescribed based on two different data sets. Results indicate that the model promisingly reproduces the observed magnitude and temporal variability of riverine nitrate concentrations. Further, nitrate delivery from lake‐specific contributing subbasins to the Great Lakes shorelines is quantified across fertilizer application scenarios. When normalized by lake volume, nitrate delivery was highest in Lakes Erie and Ontario, with concentrations of approximately 9.5 and 2.2 tons km ⁻³ , respectively. An analysis of simulation minus observation reveals a modest bias toward underestimating nitrate concentrations across observation sites, partly driven by low streamflow bias and elevated numerical dispersion inherent in the transport scheme. Overall, the implemented nitrate routing framework provides a physically‐based representation of large‐scale riverine nitrate transport and transformations and establishes a basis for future model development and nutrient assessments within CLM5‐MOSART.

Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.

Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.