CMIP7 Data Request: Earth System Priorities and Opportunities

Abstract. Simulation of the carbon cycle in climate models is important due to its impact on climate change, but many weaknesses in its reproduction were found in previous models. Improvements in the representation of the land carbon cycle in Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) include the interactive treatment of both the carbon and nitrogen cycles, improved photosynthesis, and soil hydrology. To assess the impact of these model developments on aspects of the global carbon cycle, the Earth System Model Evaluation Tool (ESMValTool) is expanded to compare CO2-concentration- and CO2-emission-driven historical simulations from CMIP5 and CMIP6 to observational data sets. A particular focus is on the differences in models with and without an interactive terrestrial nitrogen cycle. Overestimations of photosynthesis (gross primary productivity (GPP)) in CMIP5 were largely resolved in CMIP6 for participating models with an interactive nitrogen cycle but remaining for models without one. This points to the importance of including nutrient limitation. Simulating the leaf area index (LAI) remains challenging, with a large model spread in both CMIP5 and CMIP6. In ESMs, the global mean land carbon uptake (net biome productivity (NBP)) is well reproduced in the CMIP5 and CMIP6 multi-model means. However, this is the result of an underestimation of NBP in the Northern Hemisphere, which is compensated by an overestimation in the Southern Hemisphere and the tropics. Carbon stocks remain a large uncertainty in the models. While vegetation carbon content is slightly better represented in CMIP6, the inter-model range of soil carbon content remains the same between CMIP5 and CMIP6. Overall, a slight improvement in the simulation of land carbon cycle parameters is found in CMIP6 compared to CMIP5, but with many biases remaining, further improvements of models in particular for LAI and NBP is required. Models from modeling groups participating in both CMIP phases generally perform similarly or better in their CMIP6 compared to their CMIP5 models. This improvement is not as significant in the multi-model means due to more new models in CMIP6, especially those using older versions of the Community Land Model (CLM). Emission-driven simulations perform just as well as the concentration-driven models, despite the added process realism. Due to this, we recommend that ESMs in future Coupled Model Intercomparison Project (CMIP) phases perform emission-driven simulations as the standard so that climate–carbon cycle feedbacks are fully active. The inclusion of the nitrogen limitation led to a large improvement in photosynthesis compared to models not including this process, suggesting the need to view the nitrogen cycle as a necessary part of all future carbon cycle models. Possible benefits when including further limiting nutrients such as phosphorus should also be considered.

One of the major globally relevant systematic biases in previous generations of climate models has been an equatorward bias in the latitude of the Southern Hemisphere (SH) mid‐latitude tropospheric eddy driven westerly jet. The far‐reaching implications of this for Southern Ocean heat and carbon uptake and Antarctic land and sea ice are key reasons why addressing this bias is a high priority. It is therefore of primary importance to evaluate the representation of the SH westerly jet in the latest generation of global climate and earth system models that comprise the Coupled Model Intercomparison Project Phase 6 (CMIP6). In this paper we assess the representation of major indices of SH extratropical atmospheric circulation in CMIP6 by comparison against both observations and the previous generation of CMIP5 models. Indices assessed are the latitude and speed of the westerly jet, variability of the Southern Annular Mode (SAM), and representation of the Amundsen Sea Low (ASL). These are calculated from the historical forcing simulations of both CMIP5 and CMIP6 for time periods matching available observational and reanalysis data sets. From the 39 CMIP6 models available at the time of writing there is an overall reduction in the equatorward bias of the annual mean westerly jet from 1.9° in CMIP5 to 0.4° in CMIP6 and from a seasonal perspective the reduction is clearest in austral spring and summer. This is accompanied by a halving of the bias of SAM decorrelation timescales compared to CMIP5. However, no such overall improvements are evident for the ASL.

The rapid expansion of Earth system model (ESM) data available from the Coupled Model Intercomparison Project Phase 6 (CMIP6) necessitates new methods to evaluate the performance and suitability of ESMs used for hydroclimate applications as these extremely large data volumes complicate stakeholder efforts to use new ESM outputs in updated climate vulnerability and impact assessments. We develop an analysis framework to inform ESM sub‐selection based on process‐oriented considerations and demonstrate its performance for a regional application in the US Pacific Northwest. First, a suite of global and regional metrics is calculated, using multiple historical observation datasets to assess ESM performance. These metrics are then used to rank CMIP6 models, and a culled ensemble of models is selected using a trend‐related diagnostics approach. This culling strategy does not dramatically change climate scenario trend projections in this region, despite retaining only 20% of the CMIP6 ESMs in the final model ensemble. The reliability of the culled trend projection envelope and model response similarity is also assessed using a perfect model framework. The absolute difference in temperature trend projections is reduced relative to the full ensemble compared to the model for each SSP scenario, while precipitation trend errors are largely unaffected. In addition, we find that the spread of the culled ensemble temperature and precipitation trends includes the trend of the “truth” model ∼83%‐92% of the time. This analysis demonstrates a reliable method to reduce ESM ensemble size that can ease use of ESMs for creating and understanding climate vulnerability and impact assessments.

Abstract. The uptake of anthropogenic carbon (Cant) by the ocean leads to ocean acidification, causing the reduction of pH and the saturation states of aragonite (Ωarag) and calcite (Ωcalc). The Arctic Ocean is particularly vulnerable to ocean acidification due to its naturally low pH and saturation states and due to ongoing freshening and the concurrent reduction in total alkalinity in this region. Here, we analyse ocean acidification in the Arctic Ocean over the 21st century across 14 Earth system models (ESMs) from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared to the previous model generation (CMIP5), models generally better simulate maximum sea surface densities in the Arctic Ocean and consequently the transport of Cant into the Arctic Ocean interior, with simulated historical increases in Cant in improved agreement with observational products. Moreover, in CMIP6 the inter-model uncertainty of projected changes over the 21st century in Arctic Ocean Ωarag and Ωcalc averaged over the upper 1000 m is reduced by 44–64 %. The strong reduction in projection uncertainties of Ωarag and Ωcalc can be attributed to compensation between Cant uptake and total alkalinity reduction in the latest models. Specifically, ESMs with a large increase in Arctic Ocean Cant over the 21st century tend to simulate a relatively weak concurrent freshening and alkalinity reduction, while ESMs with a small increase in Cant simulate a relatively strong freshening and concurrent total alkalinity reduction. Although both mechanisms contribute to Arctic Ocean acidification over the 21st century, the increase in Cant remains the dominant driver. Even under the low-emissions Shared Socioeconomic Pathway 1-2.6 (SSP1-2.6), basin-wide averaged Ωarag undersaturation in the upper 1000 m occurs before the end of the century. While under the high-emissions pathway SSP5-8.5, the Arctic Ocean mesopelagic is projected to even become undersaturated with respect to calcite. An emergent constraint identified in CMIP5 which relates present-day maximum sea surface densities in the Arctic Ocean to the projected end-of-century Arctic Ocean Cant inventory is found to generally hold in CMIP6. However, a coincident constraint on Arctic declines in Ωarag and Ωcalc is not apparent in the new generation of models. This is due to both the reduction in Ωarag and Ωcalc projection uncertainty and the weaker direct relationship between projected changes in Arctic Ocean Cant and changes in Ωarag and Ωcalc.

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets published for 'CMIP6.RFMIP.MIROC.MIROC6.piClim-histaer' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The Earth System Model MIROC6, released in 2017, includes the components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 100 km, atmos: 250 km, land: 100 km, ocean: 100 km, seaIce: 100 km. Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions, and the results will undoubtedly be relied on by authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated at a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by World Data Centre for Climate (WDCC) at DKRZ. The project includes simulations from about 90 global climate models and around 40 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.

Abstract. Forests are an important component in the framework of nature-based solutions for mitigating climate change. However, there are still uncertainties about the biogeophysical effects of forest cover changes affecting heat and water fluxes as captured by Earth System Models (ESMs) simulations and observations. In this study, we investigate the differences in the surface temperature response to idealized, complete deforestation and the temperature sensitivity to percentage change in forest cover in ESMs and observations. In this comparison, the separation between local (at the place of deforestation) and non-local (nearby or distant locations) effects is crucial as observations capture only the former. Here, we propose a modified methodology to separate local and non-local effects in climate models suitable for simulations with linear rate of deforestation. The local sensitivity of a climate variable per unit deforested area is represented by the slope of the linear regression, where tree cover is an explanatory variable. The non-local effect is defined as the difference between the overall change in the respective climate variable and the local effect. Our analysis of eleven ESMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) that participated in the idealized global deforestation experiment deforest-glob, reveals a coherent local temperature response among climate models characterized by warming in the tropics and cooling in the northern higher latitudes. The temperature response however varies in magnitude, space and time with ESMs showing distinctive seasonal and spatial patterns. A closer look at the albedo response to deforestation across northern latitudes shows an overestimation in the ESMs in comparison to observations that translates via an emergent constraint (i.e. resulting from the linear relationships between local albedo and surface temperature within the model ensemble) into an overestimation of the overall simulated cooling effect. The overestimation of the local albedo sensitivity cannot be explained solely by the higher percentage of snow cover in ESMs. In terms of local latent heat flux sensitivity, the ESMs ensemble mean is overestimated for the boreal region, but it is in good agreement with the observational constraint in the temperate forests and the tropics. However, the inter-model spread and the internal model variation in these regions are considerable. ESMs having higher local albedo and latent heat flux sensitivities than the current observational constraints can still exhibit a realistic temperature response due to compensatory effects between the two sensitivities. Non-local effects contribute to consistent cooling throughout the globe, which persists also during the summer when the influence of the overestimated albedo sensitivity over snow is weaker. Having a deeper understanding of how local and non-local biogeophysical effects are represented in ESMs can give us insights into the net climate impact of deforestation and help us improve next generation ESMs.

Statistical downscaling is a computationally efficient approach in producing a large ensemble of fine-resolution future climate projections to support local climate adaptation efforts. Here, we propose a deep learning–based framework, named BC_DLD, to downscale the Coupled Model Intercomparison Project phase 6 (CMIP6) Earth system model (ESM) outputs from the coarse native resolution to a locally relevant fine resolution at kilometer scale and apply it to the Northeast United States, a region experiencing rapid increase of heavy precipitation and in great need for locally actionable climate information. Employing a two-phase multivariate approach by utilizing an empirical-quantile delta mapping (E-QDM) approach for bias correction at a coarse resolution and deep convolutional neural networks (CNNs) for downscaling the corrected data to a finer spatial resolution, BC_DLD is designed to preserve the spatial characteristics from observations while retaining the temporal characteristics and climate change signals from ESMs. Here, we demonstrate the implementation of BC_DLD by downscaling the outputs of GFDL-ESM4 historical run and future shared socioeconomic pathway (SSP) 5–8.5 scenario over the Northeast, from ∼100 to ∼25 km [using fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) as reference] and then further down to ∼6 km (using Livneh data as reference), and compare the downscaled data with an existing product, localized constructed analogs, version 2 (LOCA2). Despite using the same observational training data as LOCA2, BC_DLD produces more intense and rapid future increase of extreme precipitation than LOCA2, alleviating the underestimation of extremes common among statistical downscaling products. The emergent relationship between extreme precipitation intensity and temperature in BC_DLD more closely resembles the observations, further establishing its efficacy in producing useful and usable future climate projections.

A remaining carbon budget (RCB) estimates how much CO2 we can emit and still reach a specific temperature target. The RCB concept is attractive since it easily communicates to the public and policymakers, but RCBs are also subject to uncertainties. The expected warming levels for a given carbon budget has a wide uncertainty range, which increases with less ambitious targets, i.e., with higher CO2 emissions and temperatures. Leading causes of RCB uncertainty are the future non-CO2 emissions, Earth system feedbacks, and the spread in the climate sensitivity among climate models. The latter is investigated in this paper, using a simple carbon cycle model and emulators of the temperature responses of the Earth System Models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble. Driving 41 CMIP6 emulators with 127 different emission scenarios for the 21st century, we find almost perfect linear relationship between maximum global surface air temperature and cumulative carbon emissions, allowing unambiguous estimates of RCB for each CMIP6 model. The range of these estimates over the model ensemble is a measure of the uncertainty in the RCB arising from the range in climate sensitivity over this ensemble, and it is suggested that observational constraints imposed on the transient climate response in the model ensemble can reduce uncertainty in RCB estimates.

Advances in weather and climate research have demonstrated the role of the stratosphere in the Earth system across a wide range of temporal and spatial scales. Stratospheric ozone loss has been identified as a key driver of Southern Hemisphere tropospheric circulation trends, affecting ocean currents and carbon uptake, sea ice, and possibly even the Antarctic ice sheets. Stratospheric variability has also been shown to affect short-term and seasonal forecasts, connecting the tropics and midlatitudes and guiding storm-track dynamics. The two-way interactions between the stratosphere and the Earth system have motivated the World Climate Research Programme's (WCRP) Stratospheric Processes and their Role in Climate's (SPARC) activity on Modelling the Dynamics and Variability of the Stratosphere-Troposphere System (DynVar) to investigate the impact of stratospheric dynamics and variability on climate. This assessment will be made possible by two new multimodel datasets. First, roughly 10 models with a well-resolved stratosphere are participating in the Coupled Model Intercomparison Project phase 5 (CMIP5), providing the first multimodel ensemble of climate simulations coupled from the stratopause to the sea floor. Second, the Stratosphere Resolving Historical Forecast Project (Strat-HFP) of WCRP's Climate Variability and Predictability (CLIVAR) program is forming a multimodel set of seasonal hind-casts with stratosphere-resolving models, revealing the impact of both stratospheric initial conditions and dynamics on intraseasonal prediction. The CMIP5 and Strat-HFP model datasets will offer an unprecedented opportunity to understand the role of the stratosphere in the natural and forced variability of the Earth system and to determine whether incorporating knowledge of the middle atmosphere improves seasonal forecasts and climate projections.

Representation of soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon climate feedbacks. The magnitude of this uncertainty can be reduced by accurate representation of environmental controllers of SOC stocks in ESMs. In this study, we used data of environmental factors, field SOC observations, ESM projections and machine learning approaches to identify dominant environmental controllers of SOC stocks and derive functional relationships between environmental factors and SOC stocks. Our derived functional relationships predicted SOC stocks with similar accuracy as the machine learning approach. We used the derived relationships to benchmark the coupled model intercomparison project phase six ESM representation of SOC stocks. We found divergent environmental control representation in ESMs in comparison to field observations. Representation of SOC in ESMs can be improved by including additional environmental factors and representing their functional relationships with SOC consistent with observations.

The El Niño-Southern Oscillation (ENSO) is the most prominent climate system in the tropical Pacific. However, its simulation, including the amplitude, phase locking, and asymmetry of its two phases, is not well reproduced by the current climate system models. In this study, the sensitivity of the ENSO simulation to the convection schemes is discussed using the Nanjing University of Information Science and Technology Earth System version 3.0 (NESM3) model. Three convection schemes, including the default, the default coupled with the stochastic multicloud model (SMCM), and the default used in the Coupled Model Intercomparison Project Phase 6 (CMIP6), are implemented. The model results reveal that the low-level cloud cover and surface net shortwave radiation are best represented over the tropical Pacific in the model containing the SMCM. The simulations of the ENSO behavior’s response to changes in the convection scheme are not uniform. The model results reveal that the model containing the SMCM performs best in terms of simulating the seasonal cycle of the sea surface temperature anomaly along the equatorial Pacific, the phase locking, and the power spectrum of ENSO but with a modest ENSO amplitude. Compared to the model containing the default convection scheme, the coupling of the default scheme and the SMCM provides a good simulation of the ENSO’s asymmetry, while the model containing the CMIP6 convection scheme outperforms the others in terms of the simulation of the ENSO’s amplitude. Two atmospheric feedback processes were further discussed to investigate the factors controlling the ENSO’s amplitude. The analyses revealed that the strongest positive atmospheric Bjerknes feedback and the thermodynamic damping of the surface net heat flux occurred in the model containing the CMIP6 convection scheme, suggesting that the atmospheric Bjerknes feedback may overwhelm the heat flux damping feedback when determining the ENSO’s amplitude. The results of this study demonstrate that perfectly modeling and predicting the ENSO is not simple, and it is still a large challenge and issue for the entire model community in the future.

Abstract. Systematic evaluation of the carbon cycle physical and biological variables simulated in Earth System Model (ESM) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP 6) is fundamental to the understanding of terrestrial ecosystems, as well as to future projections. Leaf Area Index (LAI), Gross Primary Productivity (GPP), Net Primary Productivity (NPP), Net Ecosystem Productivity (NEP) and Land Surface Temperature (LST) as key indicators of carbon cycle performance in ESM outputs, play a critical role in evaluating ecosystem functions. Assessing these metrics can provide valuable insights into the biases in model-simulated ecosystems and offer guidance for model improvement. In this study, we assessed the interannual trends performance of LAI, GPP, NPP, NEP and LST simulated by 12 CMIP6 ESMs during the historical period by using satellite LAI, NPP, NEP, LST and CSIF data as observations. The findings indicate that: (1) There are significant uncertainties in the overall trends and interannual variability in LAI, NPP, and LST captured by the CMIP6 ESM. Meanwhile, simulated GPP and NEP trends were lower than observations with discrepancies reaching 0.03 yr−1 for GPP and 2.46 gCm-2yr-1 for NEP. (2) Spatially, CMIP6 ESMs exhibited widespread underestimation of trends in LAI, GPP, NPP, and NEP across China. The MME underestimated these variables in 46.29 % (LAI), 43.47 % (GPP), 49.81 % (NPP), and 61.34 % (NEP) of the study area. Meanwhile, the simulated LST trend is underestimated in northern China, while its overestimations in western and southern China. (3) ESMs inadequate responsiveness to anthropogenic and environmental forcing and incomplete mechanistic representation of plant respiration pathways struggled accurate simulation of trends in LAI, GPP, NPP, NEP and LST.

<p>Earth system models (ESMs) need to correctly simulate the impact of climate on vegetation, as well as the feedback of vegetation on climate. Improving the skill of ESMs in representing climate—biosphere interactions is crucial to enhance predictions of climate and ecosystem functioning. Correlation and regression techniques are commonly used to study these interactions statistically, but these methods lack the ability to unravel the bidirectional nature of the climate–biosphere system. Here, we explore these interactions across multiple temporal scales by adopting a spectral Granger causality framework that allows identifying potentially inter-dependent variables. Multi-decadal remotely-sensed records are used to analyse the impact of key climatic drivers (precipitation, radiation and temperature) on vegetation (Leaf Area Index, LAI), as well as the biophysical feedback on local climate. These observational results are in turn used to benchmark a set of Coupled Model Intercomparison Project Phase 5 (CMIP5) members at the global scale.</p><p>Results show that the climate control on LAI variability increases with longer temporal scales, being the highest at inter-annual scales. Globally, precipitation is the most dominant driver of vegetation at monthly scales, particularly in (semi-)arid regions, as expected. The seasonal LAI variability in energy-driven latitudes is mainly controlled by radiation, while air temperature controls vegetation growth and decay in northern latitudes at inter-annual scales. ESMs have a tendency to over-represent the climate control on LAI dynamics, and especially the role of precipitation at inter-annual scales. Likewise, the widespread effect of LAI variability on radiation, as observed over the northern latitudes due to albedo changes, is also overestimated by the CMIP5 models. Overall, our experiments emphasise the potential of benchmarking the representation of climate—biosphere interactions in online ESMs using causal statistics in combination with observational data.</p>

Although the mass loss of land ice is projected to be the largest contribution to sea-level rise in the coming centuries, thermal expansion will also be an important contributor and its accurate projection is primordial to understanding sea-level change over (multi-)centennial timescales. Earth System Models (ESMs) are the main tools for projecting thermosteric sea-level rise. ESMs, however, are computationally demanding and therefore long, multi-centennial simulations are challenging. In this study, we use a physical-based emulator that simplifies the climate system by using three vertically stacked layers, allowing us to mimic the energy balance response of ESMs to reproduce their thermal expansion simulations. We use this emulator to extrapolate simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) from 2100 to 2300 and validate our method with CMIP6 runs that are available over that time scale. Overall, the three-layer emulator outperforms its two-layer predecessor in simulating thermal expansion up to 2300, providing a reduction of up to 78% in cumulative error for the projection period covering 2100 to 2300. Finally, we demonstrate how using temperature output from the three-layer model can help us capture non-linearities in dynamic sea-level change better than its two-layer counterpart. The latter is a first step towards building more reliable emulation approaches for oceanic processes affecting regional sea-level change.

This study evaluated the health-related weighted ultraviolet radiation (UVR) due to the total ozone content (TOC) and the aerosol optical depth (AOD) changes. Clear-sky Ultraviolet Index (UVI), daily doses, and exposure times for erythema induction (Dery and Tery) and vitamin D synthesis (DvitD and TvitD) were computed by a radiative transfer estimator. TOC and AOD data were provided by six Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). For projections, we consider four Shared Socioeconomic Pathways scenarios-SSPs (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5)-and two time-slices (near: 2041-2060 and far future: 2081-2100). UVR projections showed pronounced changes for the summer hemispheres in the far future. TOC increases in mid- and high latitudes of the Southern Hemisphere caused decreases in UVR at the summer solstice. However, projections did not indicate sun-safe exposure conditions in South America, Australia, and Southern Africa. On the contrary, exposure around solar noon from 10 to 20min will still be sufficient to induce erythema in skin type III individuals throughout this century. In southern Argentina and Chile, the UVR insufficiency for vitamin D synthesis at solar noon in skin type III remains the same during this century at the winter solstice. In the Northern Hemisphere, UVI and Dery at the summer solstice should remain high (UVI ≥ 8; Dery ~ 7.0kJm-2) in highly populated locations. Above 45 °N, UVR levels cannot be enough to synthesize vitamin D in skin type III during the boreal winter. Our results show that climate change will affect human health through excess or lack of solar UVR availability.