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Stratospheric Aerosol Injection (SAI) is a contentious geo-engineering proposal to lessen the impacts of global heating via an artificial shade of aerosols. While model results suggest that Earth’s global mean surface temperature (GMST) can indeed be stabilised via SAI, the same does not apply to other parts of the climate system. Especially the fate of a weakening Atlantic Meridional Overturning Circulation (AMOC) under SAI remains unclear.We simulate two SAI deployment scenarios in the Community Earth System Model 2 (CESM2) to study whether a weakened AMOC can be stabilised or recovered. To obtain a strong AMOC response, both scenarios follow a high GHG emission pathway. At the same time, proportionally chosen aerosol injections stabilise the GMST at 1.5K above pre-industrial conditions. The scenarios only differ in their deployment times: aerosol injections start either early (SAI 2020) or late century (SAI 2080).Both SAI scenarios reach the target GMST. However, we find that SAI only mitigates rather than decisively reverses AMOC decline. This relatively mild oceanic response stands in contrast to efficient surface cooling. As a result, both deployment scenarios lead to drastically different climate states. In particular, late-century deployment (SAI 2080) creates a striking temperature gradient from a cold northern to a warm southern hemisphere. This phenomenon potentially stems from impaired meridional heat transport of a much weaker AMOC in SAI 2080 compared to SAI 2020.Our findings mirror recent results on fast negative emission scenarios displaying a similar inter-hemispheric gradient likely connected to slow AMOC recovery. This affirms the need for carefully tailoring SAI deployment strategies should the technology ever be considered as part of the climate action portfolio.

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5. National Science Foundation (NSF)National Science Foundation (NSF); National Center for Atmospheric Research - NSF [1852977]; RUBISCO Scientific Focus Area (SFA) - Regional and Global Climate Modeling (RGCM) Program in the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science; Columbia University Presidential Fellowship; U.S. Department of Agriculture NIFA Award [2015-67003-23485]; NASA Interdisciplinary Science Program Award [NNX17AK19G]; U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science programUnited States Department of Energy (DOE) [DE-SC0008317, DESC0016188]; National Science FoundationNational Science Foundation (NSF) [DEB-1153401]; NASA's CARBON program; NASA's TE program; National Aeronautics and Space AdministrationNational Aeronautics & Space Administration (NASA) We would like to thank the reviewers for their insightful comments and helpful suggestions that improved the clarity and presentation of the manuscript. The CESM project is supported primarily by the National Science Foundation (NSF). This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under Cooperative Agreement 1852977. Computing and data storage resources, including the Cheyenne supercomputer (doi: 10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. D. M. L. was supported in part by the RUBISCO Scientific Focus Area (SFA), which is sponsored by the Regional and Global Climate Modeling (RGCM) Program in the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science. D. K. and P. G. were supported by Columbia University Presidential Fellowship. G. B., D. L. L., W. R. W., and R. Q. T. were supported by the U.S. Department of Agriculture NIFA Award 2015-67003-23485. W. R. W. and G. K. A. were supported by the NASA Interdisciplinary Science Program Award NNX17AK19G. J. B. F. and M. S. carried out the research in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged. All rights reserved. J. B. F. and M. S. were supported in part by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science program under Awards DE-SC0008317 and DESC0016188; the National Science Foundation Ecosystem Science program (DEB-1153401); and NASA's CARBON and TE programs. All model data are archived and publicly available at the UCAR/NCAR Climate Data Gateway (https://doi.org/10.5065/d6154fwh).

The Greenland Ice Sheet (GrIS) mass loss has been accelerating at a rate of about 20 ± 10 Gt/yr2 since the end of the 1990's, with around 60 % of this mass loss directly attributed to enhanced surface meltwater runoff. However, in the climate and glaciology communities, different approaches exist on how to model the different surface mass balance (SMB) components using: (1) complex physically-based climate models which are computationally expensive; (2) intermediate complexity energy balance models; (3) simple and fast positive degree day models which base their inferences on statistical principles and are computationally highly efficient. Additionally, many of these models compute the SMB components based on different spatial and temporal resolutions, with different forcing fields as well as different ice sheet topographies and extents, making inter-comparison difficult. In the GrIS SMB model intercomparison project (GrSMBMIP) we address these issues by forcing each model with the same data (i.e., the ERA-Interim reanalysis) except for two global models for which this forcing is limited to the oceanic conditions, and at the same time by interpolating all modelled results onto a common ice sheet mask at 1 km horizontal resolution for the common period 1980–2012. The SMB outputs from 13 models are then compared over the GrIS to (1) SMB estimates using a combination of gravimetric remote sensing data from GRACE and measured ice discharge, (2) ice cores, snow pits, in-situ SMB observations, and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Our results reveal that the mean GrIS SMB of all 13 models has been positive between 1980 and 2012 with an average of 340 ± Gt/yr, but has decreased at an average rate of −7.3 Gt/yr2 (with a significance of 96 %), mainly driven by an increase of 8.0 Gt/yr2 (with a significance of 98 %) in meltwater runoff. Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting the need for accurate representation of the GrIS ablation zone extent and processes driving the surface melt. In addition, a higher density of in-situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 mWE/yr due to large discrepancies in modelled snowfall accumulation. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of same order than RCMs with observations and remain then useful tools for long-term simulations. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present day SMB relative to observations, suggesting that biases are not systematic among models.

The mass balance of the Greenland ice sheet (GrIS, units Gt per year) equals the surface mass balance (SMB) minus solid ice discharge across the grounding line. As the latter is definite positive, an important threshold for irreversible GrIS mass loss occurs when long-term average SMB becomes negative. For this to happen, runoff (mainly meltwater, some rain) must exceed mass accumulation (mainly snowfall minus sublimation). Even for a single year, this threshold has not been passed since at least 1958, the first year with reliable estimates of SMB components, although recent years with warm summers (e.g. 2012 and 2019) came close. Simply extrapolating the recent (1991-present) negative SMB trend into the future suggests that the SMB = 0 threshold could be reached before ~2040, but such predictions are extremely uncertain given the very large interannual SMB variability, the relative brevity of the time series and the uncertainty in future warming. In this study we use a cascade of models, extensively evaluated with in-situ and remotely sensed (GRACE) SMB observations, to better constrain the future regional warming threshold for the 5-year average GrIS SMB to become negative. To this end, a 1950-2100 climate change run with the global model CESM2 (app. 100 km resolution) was dynamically downscaled using the regional climate model RACMO2 (app. 11 km), which in turn was statistically downscaled to 1 km resolution. The result is a threshold regional Greenland warming of close to 4 degrees. We then use a range of CMIP5 and CMIP6 global climate models to translate the regional value into a global warming threshold for various warming scenarios, including its timing this century. We find substantial differences, ranging from stabilization before the threshold is reached in the RCP/SSP2.6 scenarios with a limited but still significant sea-level rise contribution (< 5 cm by 2100) to an imminent crossing of the warming threshold for the RCP/SSP8.5 scenarios with substantial and ever-growing contributions to sea level rise (> 10 cm by 2100). These results stress the need for strong mitigation to avoid irreversible GrIS mass loss. We finish by discussing the caveats and uncertainties of our approach.

We have compared the surface mass (SMB) and energy balance of the Earth System model (ESM) CESM (Community Earth System Model) with those of the regional climate model (RCM) MAR (Modèle Atmosphérique Régional) forced by CESM over the present era (1981 — 2010) and the future (2011 — 2100 with SSP585 scenario).Until now, global climate models (GCM) and ESMs forcing RCMs such as MAR didn’t include a module able to simulate snow and energy balance at the surface of a snow pack like the SISVAT module of MAR and were therefore not able to simulate the SMB of an ice sheet. Evaluating the added value of an RCM compared to a GCM could only be done by comparing atmospheric outputs (temperature, wind, precipitation …) in both models. CESM is the first ESM including a land model capable of simulating the surface of an ice sheet and thus to directly compare the SMB of an RCM and an ESM the first time.Our results show that, if the SMB and is components are very similar in CESM and MAR over the present era, they quickly start to diverge in our future projection, the SMB of MAR decreasing more than that of CESM. This difference in SMB evolution is almost exclusively explained by a much larger increase of the melter runoff in MAR compared to CESM whereas the temporal evolution of snowfall, rainfall and sublimation is comparable in both runs.

We propose new testbed model experiments for the Geoengineering Model Intercomparison Project (GeoMIP) that are designed to limit global warming to 1.5 °C or 2.0 °C above 1850–1900 conditions using stratospheric aerosol geoengineering (SAG). The new modeling experiments use the overshoot scenario defined in CMIP6 (SSP5-34-OS) as a baseline scenario and are designed to reduce side effects of SAG in reaching three temperature targets: global mean surface temperature, and inter-hemispheric and pole-to-equator surface temperature gradients. We further compare results to another SAG simulation using a high emission scenario (SSP5-85) as a baseline scenario in order to investigate the dependency of impacts using different injection amounts to offset different amounts of warming by SAG. The new testbed simulations are performed with the CESM2(WACCM6). We use a feedback algorithm that identifies the needed amount of sulfur dioxide injections in the stratosphere at four predefined latitudes, 30° N, 15° N, 15° S, and 30° S, to reach the three temperature targets. Here we analyze climate variables and quantities that matter for societal and ecosystem impacts. We find that changes from present day conditions (2015–2025) in some variables depend strongly on the defined temperature target (1.5 °C vs 2.0 °C). These include surface air temperature and related impacts, the Atlantic Meridional Overturning Circulation (AMOC), which impacts ocean net primary productivity, and changes in ice sheet surface mass balance, which impacts sea-level rise. Others, including global precipitation changes and the recovery of the Antarctic ozone hole, depend strongly on the amount of SAG application. Furthermore, land net primary productivity as well as ocean acidification depend mostly on the global atmospheric CO2 concentration and therefore the baseline scenario. Multi-model comparisons of the experiments proposed here would help identify consequences of scenarios that include strong mitigation, carbon dioxide removal with some SAG application, on societal impacts and ecosystems.

Here we present a long-term (1850–2200) best estimate of Greenland ice sheet (GrIS) freshwater runoff that improves spatial detail of runoff locations and temporal resolution. Ice discharge is taken from observations since 2000 and assumed constant in time. Surface meltwater runoff is retrieved from regional climate model output for the recent past and parameterized for the future based on significant correlations between runoff and midtropospheric (500 hPa) summer temperature changes over the GrIS. The simplicity of this approach enables assimilation of meltwater runoff into coupled climate models, which is demonstrated here in a case study with the medium-resolution (1?) Community Earth System Model. The model results suggest that the decrease in Atlantic Meridional Overturning Circulation (AMOC) is dominated by warming of the surface ocean and enhanced GrIS freshwater forcing leads to a slightly enhanced (?1.2 sverdrup in the 21st century) weakening of the AMOC.