Development Of Earth System Models Research Articles

Integrating Farmers’ Perspectives into Earth System Model Development: Interviews with End Users in the Willamette Valley, Oregon, to Guide Actionable Science

Abstract This paper analyzes findings from semistructured interviews and focus groups with 31 farmers in the Willamette Valley in which farmers were asked about their needs for climate data and about the usability of a range of outputs from the Community Earth System Model, version 2 (CESM2), for their soil management practices. Findings indicate that climate and soils data generated from CESM and other Earth system models (ESMs), despite their coarse spatial scale resolutions, can inform farmers’ long-term decisions, but that the data would be more usable if the outputs were provided in a format that allowed farmers to choose the variables and thresholds relevant to their particular needs and if ESMs incorporated farmer practices including residue removal, cover cropping, and tillage levels into the model operations so that farmers could better understand the impacts of their decisions. Findings also suggest that although there is a significant gap in the spatial resolution at which these global ESMs generate data and the spatial resolution needed by farmers to make most decisions, farmers are adept at making scalar adjustments to apply coarse-resolution data to the specifics of their own farm’s microclimate. Thus, our findings suggest that, to support agricultural decision-making, development priorities for ESMs should include developing better representations of agricultural management practices within the models and creating interactive data dashboards or platforms.

Microbial competition for phosphorus limits the CO2 response of a mature forest

The capacity for terrestrial ecosystems to sequester additional carbon (C) with rising CO2 concentrations depends on soil nutrient availability1,2. Previous evidence suggested that mature forests growing on phosphorus (P)-deprived soils had limited capacity to sequester extra biomass under elevated CO2 (refs. 3–6), but uncertainty about ecosystem P cycling and its CO2 response represents a crucial bottleneck for mechanistic prediction of the land C sink under climate change7. Here, by compiling the first comprehensive P budget for a P-limited mature forest exposed to elevated CO2, we show a high likelihood that P captured by soil microorganisms constrains ecosystem P recycling and availability for plant uptake. Trees used P efficiently, but microbial pre-emption of mineralized soil P seemed to limit the capacity of trees for increased P uptake and assimilation under elevated CO2 and, therefore, their capacity to sequester extra C. Plant strategies to stimulate microbial P cycling and plant P uptake, such as increasing rhizosphere C release to soil, will probably be necessary for P-limited forests to increase C capture into new biomass. Our results identify the key mechanisms by which P availability limits CO2 fertilization of tree growth and will guide the development of Earth system models to predict future long-term C storage.

Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3

Abstract. Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction.

Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach

Abstract. Atmospheric aerosols have important impacts on air quality and the Earth–atmospheric energy balance. However, as computing power is limited, Earth system models generally use coarse spatial grids and parameterize finer-scale atmospheric processes. These parameterizations and the simulation of atmospheric aerosols are often sensitive to model horizontal resolutions. Understanding the sensitivities is necessary for the development of Earth system models at higher resolutions with the deployment of more powerful supercomputers. Using the Energy Exascale Earth System Model (E3SM) version 1, this study investigates the impact of horizontal grid spacing on the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols (ERFaer) over the contiguous United States. We examine the resolution sensitivity by comparing the nudged simulation results for 2016 from the low-resolution model (LR) and the regional refinement model (RRM). As expected, the simulated emissions of natural dust, sea salt, and marine organic matter are substantially higher in the RRM than in the LR. In addition, RRM simulates stronger aqueous-phase production of sulfate through the enhanced oxidation of sulfur dioxide by hydrogen peroxide due to increased cloud liquid water content. In contrast, the gas-phase chemical production of sulfate is slightly suppressed. The RRM resolves more large-scale precipitation and produces less convective precipitation than the LR, leading to increased (decreased) aerosol wet scavenging by large-scale (convective) precipitation. Regarding aerosol effects on clouds, RRM produces larger temporal variabilities in the large-scale liquid cloud fractions than LR, resulting in increased microphysical cloud processing of aerosols (more interstitial aerosols are converted to cloud-borne aerosols via aerosol activation) in RRM. Water vapor condensation is also enhanced in RRM compared to LR. Consequently, the RRM simulation produces more cloud droplets, a larger cloud droplet radius, a higher liquid water path, and a larger cloud optical depth than the LR simulation. A comparison of the present-day and pre-industrial simulations indicates that, for this contiguous United States domain, the higher-resolution increases ERFaer at the top of the model by about 12 %, which is mainly attributed to the strengthened indirect effect associated with aerosol–cloud interactions.

Measuring Turbulent Water Vapor Fluxes Using a Tunable Diode Laser-Based Open-Path Gas Analyzer

The reliable observation and accurate estimates of land–atmosphere water vapor (H2O) flux is essential for ecosystem management and the development of Earth system models. Currently, the most direct measurement method for H2O flux is eddy covariance (EC), which depends on the development of fast-response H2O sensors. In this study, we presented a cost-efficient open-path H2O analyzer (model: HT1800) based on the tunable diode laser absorption spectroscopy (TDLAS) technique, and investigated its applicability for measuring atmospheric turbulent flux of H2O using the EC method. We prepared two HT1800 analyzers with lasers that operate at wavelengths of 1392 nm and 1877 nm, respectively. The field performance of the two analyzers was evaluated through inter-comparative experiments with LI-7500RS and IRGASON, two of the most commonly used H2O analyzers in the EC community. Water vapor densities measured by the three types of analyzers had high overall agreement with the reference sensor; however, they all experienced drift. The mean density drifts of HT1800, LI-7500 and IRGASON were 3.7–5.2%, 4.0% and 3.8%, respectively. Even so, the half-hourly H2O fluxes measured by HT1800 were highly consistent with those by LI-7500RS and IRGASON (with a difference of less than 2%), suggesting that HT1800 can obtain H2O fluxes with high confidence. The HT1800 was also proved to be suitable for EC application in terms of data availability, flux detection limit and response to the high-frequency turbulent variation. Furthermore, we investigated how the spectroscopic effect influences the measurements of H2O density and flux. Despite the fact that the 1392 nm laser was much more susceptible to the spectroscopic effect, the fluxes after correcting for this bias showed excellent agreement with the IRGASON fluxes. Considering the cost advantage in laser and photodetector, the HT1800 analyzer using a 1392 nm infrared laser is a promising and economical solution for EC measurement studies of water vapor.

Spatiotemporally heterogeneous soil thermohydraulic processes in the frozen soil of the Tibetan Plateau

Frozen soil properties and thermohydraulic processes are still not fully understood due to lack of in-situ measurements, especially in the high altitudes and high latitudes (HAHL). Based on hourly measurements at 10, 40, 80 and 120 cm depths at 21 sites in the west, south and northeast regions of the Tibetan Plateau (TP) during July 2018 - July 2019, we examined soil particles, spatiotemporal variations of soil thermohydraulic processes and their connections with environmental factors to reveal the heterogenous frozen soil properties on the TP. Sand and silt are the dominant soil particles and clay is less than 10% at the sites. Existing and widely used soil products underestimates (significantly overestimates) sand (clay) content on the TP which raises the uncertainty in the thermohydraulic parameters derived from these products. Diurnal soil moisture and temperature variations are seen only above 40 cm, but seasonal variations occur down to 120 cm due to the soil memory effect. Seasonally frozen soil and permafrost soil show different freezing and melting processes. Dry soil features greater soil temperature temporal variability and deeper maximum frozen depth than wet soil. Zero curtain occurs in both dry and wet soil, and displays high (low) frequency but short (long) duration at 10 (80) cm that vary spatially. Moisture depression exists in seasonally frozen soil and is determined by the initial soil moisture and temperature gradient strength. The strong thermohydraulic coupling existed in cold season collapses in warm season. Soil moisture exhibits higher spatiotemporal variability than soil temperature. At daily time scale, the influences of precipitation, wind speed and relative humidity on soil moisture and temperature vary under different climate conditions. These findings fill the knowledge gaps in the soil thermohydraulic processes in the HAHL, and improve the understanding of frozen soil properties and heat-water coupling processes in soil in the HAHL. The study will benefit the Earth system model development and improve the quality of soil temperature and moisture assimilation and remote sensing products in the HAHL.

FROM-GLC Plus: toward near real-time and multi-resolution land cover mapping

ABSTRACT Global land cover has undergone extensive and rapid changes as a result of human activities and climate change. These changes have had a significant impact on biodiversity, the surface energy balance, and sustainable development. Global land cover data underpins research on the development of earth system models, resource management, and evaluation of the ecological environment. However, there are limitations in the classification detail, spatial resolution, and rapid change monitoring capability of global land cover change data. Building on the earlier Global Land Cover Mapping (Finer Resolution Observation and Monitoring – Global Land Cover, FROM-GLC), we developed the improved Global Land Cover Change Monitoring Platform (FROM-GLC Plus) using methods such as multi-season sample space-time migration, multi-source data time series reconstruction, and machine learning. The FROM-GLC Plus system provides a capacity for producing global land cover change data set from the 1980s with flexibility in spatio–temporal details. The preliminary results show that FROM-GLC Plus provides a framework for near real-time land cover mapping at multi-temporal (annual to daily) and multi-resolution (30 m to sub-meter) levels.

Toward Efficient Calibration of Higher‐Resolution Earth System Models

AbstractProjections of future climate change to support decision‐making require Earth system models (ESMs) running at high spatial resolution, but at present this is computationally prohibitive. A major challenge is the calibration (parameter tuning) during the development of ESMs, which requires running large numbers of simulations to identify optimal values for parameters that are poorly constrained by observations. Here, we train a convolutional neural network (CNN) to emulate perturbed parameter ensembles from two lower‐resolution (and thus much less expensive) versions of the same ESM, and a smaller number of higher‐resolution simulations. Cross‐validated results show that the CNN's skill exceeds that of a climatological baseline for most variables with as few as 5–10 examples of the higher‐resolution ESM, and for all variables (including precipitation) with at least 20 examples. This proof‐of‐concept study demonstrates a machine learning based approach that makes the process of constructing a higher‐resolution emulator 20%–40% more computationally efficient, and thus offers the prospect of significantly more efficient calibration of ESMs.

Global Environmental Change Perspectives on Integrated, Coordinated, Open, and Networked (ICON) Science

AbstractThis article is composed of two independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles (Goldman et al., 2021, https://doi.org/10.1029/2021eo153180) in Global Environmental Change and discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (Section 2) Field, experimental, remote sensing, and real‐time data research and application; (Section 3) Earth system modeling. In Section 2 we stress the inherently integrated nature of global environmental change, which necessitates an integrated approach for the planning, execution and analysis of measurement campaigns, while highlighting opportunities for wider adoption of the other ICON principles. In Section 3 the highly integrated and networked nature of Earth system model development is discussed, with suggestions for how to increase openness and coordination to enhance model application by end‐user and stakeholder communities.

Earth system models for regional environmental management of red tide: Prospects and limitations of current generation models and next generation development.

Earth system models (ESMs) serve as a unique research infrastructure for quality climate services, yet their application for environmental management at regional scale has not yet been fully explored. The unprecedented resolution and model fidelity of the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations, especially of the High-Resolution Model Intercomparison Project (HighResMIP) focusing on regional phenomena, offer opportunities for such applications. This article presents the first venture into using the HighResMIP simulations to tackle a regional environmental issue, the Florida Red Tide. This is a harmful algae bloom caused by the dinoflagellate Karenia brevis, a toxic single-celled microscopic protist. We use CMIP6 historical simulations to establish a causal agreement between the position of Loop Current, a warm ocean current that moves into the Gulf of Mexico, and the occurrence of K. brevis blooms on the Western Florida shelf. Results show that the high-resolution ESMs are capable of simulating the phenomena of interest (i.e., Loop Current) at the regional spatial scale with generally adequate data-model agreement in the context of the relation between Loop Current and red tide. We use this case study to elaborate on the prospects and limitations of using publicly available CMIP data for regional environmental management. We highlight the current gaps and the developmental needs for the next generation ESMs, and discuss the role of stakeholder participation in future ESMs development to facilitate the translation of scientific understanding to better inform decision-making of regional environmental management.

Earth and Space Science Informatics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

AbstractThis article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles (Goldman, et al., 2021b, https://doi.org/10.1029/2021EO153180) in Earth and Space Science Informatics (ESSI) and includes discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (Section 2) Global collaboration, cyberinfrastructure, and data sharing; (Section 3) Machine learning for multiscale modeling; (Section 4) Aerial and satellite remote sensing for advancing Earth system model development by integrating field and ancillary data. ESSI addresses data management practices, computation and analysis, and hardware and software infrastructure. Our role in ICON science therefore involves collaborative work to assess, design, implement, and promote practices and tools that enable effective data management, discovery, integration, and reuse for interdisciplinary work in Earth and space science disciplines. Networks of diverse people with expertise across Earth, space, and data science disciplines are essential for efficient and ethical exchanges of findable, accessible, interoperable, and reusable (FAIR) research products and practices. Our challenge is then to coordinate the development of standards, curation practices, and tools that enable integrating and reusing multiple data types, software, multi‐scale models, and machine learning approaches across disciplines in a way that is as open and/or FAIR as ethically possible. This is a major endeavor that could greatly increase the pace and potential of interdisciplinary scientific discovery.

Modeling the Joint Effects of Vegetation Characteristics and Soil Properties on Ecosystem Dynamics in a Panama Tropical Forest

AbstractIn tropical forests, both vegetation characteristics and soil properties are important not only for controlling energy, water, and gas exchanges directly but also determining the competition among species, successional dynamics, forest structure and composition. However, the joint effects of the two factors have received limited attention in Earth system model development. Here we use a vegetation demographic model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM‐FATES, to explore how plant traits and soil properties affect tropical forest growth and composition concurrently. A large ensemble of simulations with perturbed vegetation and soil hydrological parameters is conducted at the Barro Colorado Island, Panama. The simulations are compared against observed carbon, energy, and water fluxes. We find that soil hydrological parameters, particularly the scaling exponent of the soil retention curve (Bsw), play crucial roles in controlling forest diversity, with higher Bsw values (>7) favoring late successional species in competition, and lower Bsw values (1 ∼ 7) promoting the coexistence of early and late successional plants. Considering the additional impact of soil properties resolves a systematic bias of FATES in simulating sensible/latent heat partitioning with repercussion on water budget and plant coexistence. A greater fraction of deeper tree roots can help maintain the dry‐season soil moisture and plant gas exchange. As soil properties are as important as vegetation parameters in predicting tropical forest dynamics, more efforts are needed to improve parameterizations of soil functions and belowground processes and their interactions with aboveground vegetation dynamics.

The evolution of biogeochemistry: revisited

The evolution of biogeochemistry, retraces the important historical steps in part, covered by Gorham (Biogeochemistry 13:199–239, 1991) in the 18–19th centuries—with new emergent linkages and trends in 20–21st centuries. In the post-phlogiston period, key synthetic connections are made between weathering, atmospheric chemistry, carbon cycling, and climate change. Early work in the 19th century, focused on weathering and the importance of organisms in the exchange of carbon dioxide between the rocks and the atmosphere, provided foundations for new analytical approaches. The role microbes in connecting abiotic and biotic processes begins to emerge, based largely on the existing knowledge of stoichiometry in agricultural soils and plants. This in part, leads to the founding of ecology and its linkages with evolution and biogeography. Verandsky boldly emerges in the 20th century, with his concepts of a biosphere and a noosphere, as concerns begin to arise about human impacts on nature. The development of organic geochemistry as a discipline, allowed for new roots to develop in the evolution of biogeochemistry through linkages between short and long-term carbon cycles. In the 20th century, a new interesting stoichiometry emerges in biogeochemistry—as related to the Green Revolution, human population growth, and eutrophication problems. The advent of long-term and large-scale experiments help to constrain the complexity of non-linearity and regional differences in fluxes and rates in biogeochemical work. A new age begins in the 21st century whereby molecular approaches (e.g. omics) combined with large-scale satellite, monitoring, survey, observatory approaches are combined in the development of Earth System models. These new connections with ecological/evolutionary genetics are one of the more dramatic and important aspects of biogeochemistry in modern times.

A Causal Inference Model Based on Random Forests to Identify the Effect of Soil Moisture on Precipitation

AbstractSoil moisture influences precipitation mainly through its impact on land–atmosphere interactions. Understanding and correctly modeling soil moisture–precipitation (SM–P) coupling is crucial for improving weather forecasting and subseasonal to seasonal climate predictions, especially when predicting the persistence and magnitude of drought. However, the sign and spatial structure of SM–P feedback are still being debated in the climate research community, mainly due to the difficulty in establishing causal relationships and the high degree of nonlinearity in land–atmosphere processes. To this end, we developed a causal inference model based on the Granger causality analysis and a nonlinear machine learning model. This model includes three steps: nonlinear anomaly decomposition, nonlinear Granger causality analysis, and evaluation of the quality of SM–P feedback, which eliminates the nonlinear response of interannual and seasonal variability and the memory effects of climatic factors and isolates the causal relationship of local SM–P feedback. We applied this model by using National Climate Assessment–Land Data Assimilation System (NCA-LDAS) datasets over the United States. The results highlight the importance of nonlinear atmosphere responses in land–atmosphere interactions. In addition, the strong feedback over the southwestern United States and the Great Plains both highlight the impacts of topographic factors rather than only the sensitivity of evapotranspiration to soil moisture. Furthermore, the SM–P index defined by our framework is used to benchmark Earth system models (ESMs), which provides a new metric for efficiently identifying potential model biases in modeling local land–atmosphere interactions and may help the development of ESMs in improving simulations of water cycle variability and extremes.

Adjusting application codes and optimizing funding layout for the discipline of atmospheric sciences in the National Natural Science Foundation of China

The National Natural Science Foundation of China (NSFC) launched the Reform Initiatives, and defined three major reform tasks under the guidance of the overall goal of building a national natural science funding system in the new era. One of the three tasks under this goal is to optimize the funding layout in order to support convergence between knowledge and application according to the inherent logic and landscape of the knowledge systems. The application code system of NSFC is of vital importance to its funding strategy and provides strong guidelines for the disciplinary layout. In 2019, the discipline of atmospheric sciences (DAS) was selected as a reform pilot in the Department of Earth Sciences (DOE) of NSFC. Based on “problem orientation”, the application code system was reviewed and significantly revised according to the top-level strategy of “Habitable Earth”, initiated by DOE. The “Habitable Earth” strategy is dedicated to promote the geosciences within the frame of Earth System Sciences, with emphasis on “deep earth”, “deep sea”, and “deep space”. In this study, the background for the code system adjustment is addressed, the reform idea and measurements are illustrated, and the structures of the new code system are set up, which are helpful for the applicants to select the appropriate codes in their proposals, and beneficial to the scientific community to better understand the future funding layout of DAS. After in-depth analysis of previous application code system, several key drawbacks are pointed out. They are but not limited to: (1) Lack of self-consistency in logical structures, (2) incomplete coverage of sub-disciplines, and (3) disconnection with the strategy of “Habitable Earth”. To address these issues, this paper proposes a new classification strategy for the application codes according to three coordinated aspects: (1) Branch disciplines, (2) supporting technologies, and (3) social development services. The effective promotion of the integration of atmospheric sciences within “Habitable Earth” is proposed by establishing cross-disciplinary codes for interdisciplinary sciences. The proposed new code system in DAS consists of 15 sub-codes, i.e., “D0501 Synoptic Meteorology”, “D0502 Climate and Climate System”, “D0503 Paleoclimatology”, “D0504 Atmospheric Dynamics”, “D0505 Atmospheric Physics”, “D0506 Atmospheric Chemistry”, “D0507 Ecological Meteorology”, “D0508 Planetary Atmosphere”, “D0509 Atmospheric Observation Techniques”, “D0510 Atmospheric Data and Information Science”, “D0511 Atmospheric Numerical Model Development”, “D0512 Earth System Model Development”, “D0513 Climate Change, its Impacts and Countermeasures”, “D0514 Atmospheric Environment and Medical Meteorology”, and “D0515 Applied Meteorology”. The first eight items (D0501 to D0508), the next four items (D0509 to D0512), and the last three items (D0513 to D0515) are categorized as “branch disciplines”, “supporting technologies”, and “social development services”, respectively. Based on the adjusted application code system, the “research directions” for each code as well as “keywords” for each direction are determined. Finally, a complete information chain for the discipline of atmospheric sciences is formed, consisting of “application codes”, “research directions”, and “keywords”. The expert information in the DAS database has begun to be updated according to the new chain. The information in the new chain will be also conducive to the application of artificial intelligence technology in assigning the proposals to appropriate reviewers and classifying the research achievements in DAS. The efforts to adjust the application codes reflect the consensus of the broad atmospheric sciences community on the development of atmospheric sciences from “traditional meteorology” to the concept of “Habitable Earth”, as well as embody NSFC’s funding orientation in the new era of China.

Including vegetation dynamics in an atmospheric chemistry-enabled general circulation model: linking LPJ-GUESS (v4.0) with the EMAC modelling system (v2.53)

Abstract. Central to the development of Earth system models (ESMs) has been the coupling of previously separate model types, such as ocean, atmospheric, and vegetation models, to address interactive feedbacks between the system components. A modelling framework which combines a detailed representation of these components, including vegetation and other land surface processes, enables the study of land–atmosphere feedbacks under global climate change. Here we present the initial steps of coupling LPJ-GUESS, a dynamic global vegetation model, to the atmospheric chemistry-enabled atmosphere–ocean general circulation model EMAC. The LPJ-GUESS framework is based on ecophysiological processes, such as photosynthesis; plant and soil respiration; and ecosystem carbon, nitrogen, and water cycling, and it includes a comparatively detailed individual-based representation of resource competition, plant growth, and vegetation dynamics as well as fire disturbance. Although not enabled here, the model framework also includes a crop and managed-land scheme, a representation of arctic methane and permafrost, and a choice of fire models; and hence it represents many important terrestrial biosphere processes and provides a wide range of prognostic trace-gas emissions from vegetation, soil, and fire. We evaluated an online one-way-coupled model configuration (with climate variable being passed from EMAC to LPJ-GUESS but no return information flow) by conducting simulations at three spatial resolutions (T42, T63, and T85). These were compared to an expert-derived map of potential natural vegetation and four global gridded data products: tree cover, biomass, canopy height, and gross primary productivity (GPP). We also applied a post hoc land use correction to account for human land use. The simulations give a good description of the global potential natural vegetation distribution, although there are some regional discrepancies. In particular, at the lower spatial resolutions, a combination of low-temperature and low-radiation biases in the growing season of the EMAC climate at high latitudes causes an underestimation of vegetation extent. Quantification of the agreement with the gridded datasets using the normalised mean error (NME) averaged over all datasets shows that increasing the spatial resolution from T42 to T63 improved the agreement by 10 %, and going from T63 to T85 improved the agreement by a further 4 %. The highest-resolution simulation gave NME scores of 0.63, 0.66, 0.84, and 0.53 for tree cover, biomass, canopy height, and GPP, respectively (after correcting tree cover and biomass for human-caused deforestation which was not present in the simulations). These scores are just 4 % worse on average than an offline LPJ-GUESS simulation using observed climate data and corrected for deforestation by the same method. However, it should be noted that the offline LPJ-GUESS simulation used a higher spatial resolution, which makes the evaluation more rigorous, and that excluding GPP from the datasets (which was anomalously better in the EMAC simulations) gave 10 % worse agreement for the EMAC simulation than the offline simulation. Gross primary productivity was best simulated by the coupled simulations, and canopy height was the worst. Based on this first evaluation, we conclude that the coupled model provides a suitable means to simulate dynamic vegetation processes in EMAC.

Machine learning and artificial intelligence to aid climate change research and preparedness

Climate change challenges societal functioning, likely requiring considerable adaptation to cope with future altered weather patterns. Machine learning (ML) algorithms have advanced dramatically, triggering breakthroughs in other research sectors, and recently suggested as aiding climate analysis (Reichstein et al 2019 Nature 566 195–204, Schneider et al 2017 Geophys. Res. Lett. 44 12396–417). Although a considerable number of isolated Earth System features have been analysed with ML techniques, more generic application to understand better the full climate system has not occurred. For instance, ML may aid teleconnection identification, where complex feedbacks make characterisation difficult from direct equation analysis or visualisation of measurements and Earth System model (ESM) diagnostics. Artificial intelligence (AI) can then build on discovered climate connections to provide enhanced warnings of approaching weather features, including extreme events. While ESM development is of paramount importance, we suggest a parallel emphasis on utilising ML and AI to understand and capitalise far more on existing data and simulations.

The match between climate services demands and Earth System Models supplies

Earth System Models (ESM) are key ingredients of many of the climate services that are currently being developed and delivered. However, ESMs have more applications than the provision of climate services, and similarly many climate services use more sources of information than ESMs. This discussion paper elaborates on dilemmas that are evident at the interface between ESMs and climate services, in particular: (a) purposes of the models versus service development, (b) gap between the spatial and temporal scales of the models versus the scales needed in applications, and (c) Tailoring climate model results to real-world applications. A continued and broad-minded dialogue between the ESM developers and climate services providers’ communities is needed to improve both the optimal use and direction of ESM development and climate service development. We put forward considerations to improve this dialogue between the communities developing ESMs and climate services, in order to increase the mutual benefit that enhanced understanding of prospects and limitations of ESMs and climate services will bring.

Deep learning to represent subgrid processes in climate models

The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly. The trained neural network then replaces the traditional subgrid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multiyear simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth system model development could play a key role in reducing climate prediction uncertainty in the coming decade.

An Overview of Interactions and Feedbacks Between Ice Sheets and the Earth System

AbstractIce sheet response to forced changes—such as that from anthropogenic climate forcing—is closely regulated by two‐way interactions with other components of the Earth system. These interactions encompass the ice sheet response to Earth system forcing, the Earth system response to ice sheet change, and feedbacks resulting from coupled ice sheet/Earth system evolution. Motivated by the impact of Antarctic and Greenland ice sheet change on future sea level rise, here we review the state of knowledge of ice sheet/Earth system interactions and feedbacks. We also describe emerging observation and model‐based methods that can improve understanding of ice sheet/Earth system interactions and feedbacks. We particularly focus on the development of Earth system models that incorporate current understanding of Earth system processes, ice dynamics, and ice sheet/Earth system couplings. Such models will be critical tools for projecting future sea level rise from anthropogenically forced ice sheet mass loss.