Geoscientific Model Development

Abstract. By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: – How does the Earth system respond to forcing? – What are the origins and consequences of systematic model biases? – How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.

Tóm tắt. Cả sai số bình phương trung bình (RMSE) và sai số tuyệt đối trung bình (MAE) đều thường được sử dụng trong các nghiên cứu đánh giá mô hình. Willmott và Matsuura (2005) đã đề xuất rằng RMSE không phải là một chỉ số tốt về hiệu suất trung bình của mô hình và có thể là một chỉ báo gây hiểu lầm về sai số trung bình, do đó MAE sẽ là một chỉ số tốt hơn cho mục đích đó. Mặc dù một số lo ngại về việc sử dụng RMSE được Willmott và Matsuura (2005) và Willmott et al. (2009) nêu ra là có cơ sở, sự đề xuất tránh sử dụng RMSE thay vì MAE không phải là giải pháp. Trích dẫn những bài báo đã nói ở trên, nhiều nhà nghiên cứu đã chọn MAE thay vì RMSE để trình bày thống kê đánh giá mô hình của họ khi việc trình bày hoặc thêm các chỉ số RMSE có thể có lợi hơn. Trong ghi chú kỹ thuật này, chúng tôi chứng minh rằng RMSE không mơ hồ trong ý nghĩa của nó, trái ngược với những gì được Willmott et al. (2009) tuyên bố. RMSE thích hợp hơn để đại diện cho hiệu suất của mô hình khi phân phối sai số được kỳ vọng là phân phối Gaussian. Ngoài ra, chúng tôi chỉ ra rằng RMSE thỏa mãn yêu cầu bất đẳng thức tam giác cho một chỉ số đo khoảng cách, trong khi Willmott et al. (2009) chỉ ra rằng các thống kê dựa trên tổng bình phương không thỏa mãn quy tắc này. Cuối cùng, chúng tôi đã thảo luận về một số tình huống mà việc sử dụng RMSE sẽ có lợi hơn. Tuy nhiên, chúng tôi không tranh cãi rằng RMSE ưu việt hơn MAE. Thay vào đó, một sự kết hợp của các chỉ số, bao gồm nhưng chắc chắn không giới hạn ở RMSEs và MAEs, thường cần thiết để đánh giá hiệu suất của mô hình.\n

Abstract. The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1) is a modeling framework for estimating fluxes of biogenic compounds between terrestrial ecosystems and the atmosphere using simple mechanistic algorithms to account for the major known processes controlling biogenic emissions. It is available as an offline code and has also been coupled into land surface and atmospheric chemistry models. MEGAN2.1 is an update from the previous versions including MEGAN2.0, which was described for isoprene emissions by Guenther et al. (2006) and MEGAN2.02, which was described for monoterpene and sesquiterpene emissions by Sakulyanontvittaya et al. (2008). Isoprene comprises about half of the total global biogenic volatile organic compound (BVOC) emission of 1 Pg (1000 Tg or 1015 g) estimated using MEGAN2.1. Methanol, ethanol, acetaldehyde, acetone, α-pinene, β-pinene, t-β-ocimene, limonene, ethene, and propene together contribute another 30% of the MEGAN2.1 estimated emission. An additional 20 compounds (mostly terpenoids) are associated with the MEGAN2.1 estimates of another 17% of the total emission with the remaining 3% distributed among >100 compounds. Emissions of 41 monoterpenes and 32 sesquiterpenes together comprise about 15% and 3%, respectively, of the estimated total global BVOC emission. Tropical trees cover about 18% of the global land surface and are estimated to be responsible for ~80% of terpenoid emissions and ~50% of other VOC emissions. Other trees cover about the same area but are estimated to contribute only about 10% of total emissions. The magnitude of the emissions estimated with MEGAN2.1 are within the range of estimates reported using other approaches and much of the differences between reported values can be attributed to land cover and meteorological driving variables. The offline version of MEGAN2.1 source code and driving variables is available from

Abstract. Projections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. In this paper, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate a wide range of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the same time, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2 °C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. To serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017–2018 time frame, and output from the climate model projections made available and analyses performed over the 2018–2020 period.

Abstract. The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised, aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include (1) a revised formulation of the evaporative stress, (2) an optimized drainage algorithm, and (3) a new soil moisture data assimilation system. GLEAM v3 is used to produce three new data sets of terrestrial evaporation and root-zone soil moisture, including a 36-year data set spanning 1980–2015, referred to as v3a (based on satellite-observed soil moisture, vegetation optical depth and snow-water equivalent, reanalysis air temperature and radiation, and a multi-source precipitation product), and two satellite-based data sets. The latter share most of their forcing, except for the vegetation optical depth and soil moisture, which are based on observations from different passive and active C- and L-band microwave sensors (European Space Agency Climate Change Initiative, ESA CCI) for the v3b data set (spanning 2003–2015) and observations from the Soil Moisture and Ocean Salinity (SMOS) satellite in the v3c data set (spanning 2011–2015). Here, these three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 91 eddy-covariance towers and 2325 soil moisture sensors across a broad range of ecosystems. Results indicate that the quality of the v3 soil moisture is consistently better than the one from v2: average correlations against in situ surface soil moisture measurements increase from 0.61 to 0.64 in the case of the v3a data set and the representation of soil moisture in the second layer improves as well, with correlations increasing from 0.47 to 0.53. Similar improvements are observed for the v3b and c data sets. Despite regional differences, the quality of the evaporation fluxes remains overall similar to the one obtained using the previous version of GLEAM, with average correlations against eddy-covariance measurements ranging between 0.78 and 0.81 for the different data sets. These global data sets of terrestrial evaporation and root-zone soil moisture are now openly available at www.GLEAM.eu and may be used for large-scale hydrological applications, climate studies, or research on land–atmosphere feedbacks.

Abstract. The Fire INventory from NCAR version 1.0 (FINNv1) provides daily, 1 km resolution, global estimates of the trace gas and particle emissions from open burning of biomass, which includes wildfire, agricultural fires, and prescribed burning and does not include biofuel use and trash burning. Emission factors used in the calculations have been updated with recent data, particularly for the non-methane organic compounds (NMOC). The resulting global annual NMOC emission estimates are as much as a factor of 5 greater than some prior estimates. Chemical speciation profiles, necessary to allocate the total NMOC emission estimates to lumped species for use by chemical transport models, are provided for three widely used chemical mechanisms: SAPRC99, GEOS-CHEM, and MOZART-4. Using these profiles, FINNv1 also provides global estimates of key organic compounds, including formaldehyde and methanol. Uncertainties in the emissions estimates arise from several of the method steps. The use of fire hot spots, assumed area burned, land cover maps, biomass consumption estimates, and emission factors all introduce error into the model estimates. The uncertainty in the FINNv1 emission estimates are about a factor of two; but, the global estimates agree reasonably well with other global inventories of biomass burning emissions for CO, CO2, and other species with less variable emission factors. FINNv1 emission estimates have been developed specifically for modeling atmospheric chemistry and air quality in a consistent framework at scales from local to global. The product is unique because of the high temporal and spatial resolution, global coverage, and the number of species estimated. FINNv1 can be used for both hindcast and forecast or near-real time model applications and the results are being critically evaluated with models and observations whenever possible.

Abstract. We present a new data set of annual historical (1750–2014) anthropogenic chemically reactive gases (CO, CH4, NH3, NOx, SO2, NMVOCs), carbonaceous aerosols (black carbon – BC, and organic carbon – OC), and CO2 developed with the Community Emissions Data System (CEDS). We improve upon existing inventories with a more consistent and reproducible methodology applied to all emission species, updated emission factors, and recent estimates through 2014. The data system relies on existing energy consumption data sets and regional and country-specific inventories to produce trends over recent decades. All emission species are consistently estimated using the same activity data over all time periods. Emissions are provided on an annual basis at the level of country and sector and gridded with monthly seasonality. These estimates are comparable to, but generally slightly higher than, existing global inventories. Emissions over the most recent years are more uncertain, particularly in low- and middle-income regions where country-specific emission inventories are less available. Future work will involve refining and updating these emission estimates, estimating emissions' uncertainty, and publication of the system as open-source software.

Abstract. An earth system model (MIROC-ESM 2010) is fully described in terms of each model component and their interactions. Results for the CMIP5 (Coupled Model Intercomparison Project phase 5) historical simulation are presented to demonstrate the model's performance from several perspectives: atmosphere, ocean, sea-ice, land-surface, ocean and terrestrial biogeochemistry, and atmospheric chemistry and aerosols. An atmospheric chemistry coupled version of MIROC-ESM (MIROC-ESM-CHEM 2010) reasonably reproduces transient variations in surface air temperatures for the period 1850–2005, as well as the present-day climatology for the zonal-mean zonal winds and temperatures from the surface to the mesosphere. The historical evolution and global distribution of column ozone and the amount of tropospheric aerosols are reasonably simulated in the model based on the Representative Concentration Pathways' (RCP) historical emissions of these precursors. The simulated distributions of the terrestrial and marine biogeochemistry parameters agree with recent observations, which is encouraging to use the model for future global change projections.

Abstract. The Joint UK Land Environment Simulator (JULES) is a process-based model that simulates the fluxes of carbon, water, energy and momentum between the land surface and the atmosphere. Many studies have demonstrated the important role of the land surface in the functioning of the Earth System. Different versions of JULES have been employed to quantify the effects on the land carbon sink of climate change, increasing atmospheric carbon dioxide concentrations, changing atmospheric aerosols and tropospheric ozone, and the response of methane emissions from wetlands to climate change. This paper describes the consolidation of these advances in the modelling of carbon fluxes and stores, in both the vegetation and soil, in version 2.2 of JULES. Features include a multi-layer canopy scheme for light interception, including a sunfleck penetration scheme, a coupled scheme of leaf photosynthesis and stomatal conductance, representation of the effects of ozone on leaf physiology, and a description of methane emissions from wetlands. JULES represents the carbon allocation, growth and population dynamics of five plant functional types. The turnover of carbon from living plant tissues is fed into a 4-pool soil carbon model. The process-based descriptions of key ecological processes and trace gas fluxes in JULES mean that this community model is well-suited for use in carbon cycle, climate change and impacts studies, either in standalone mode or as the land component of a coupled Earth system model.

Abstract. The core version of the Norwegian Climate Center's Earth System Model, named NorESM1-M, is presented. The NorESM family of models are based on the Community Climate System Model version 4 (CCSM4) of the University Corporation for Atmospheric Research, but differs from the latter by, in particular, an isopycnic coordinate ocean model and advanced chemistry–aerosol–cloud–radiation interaction schemes. NorESM1-M has a horizontal resolution of approximately 2° for the atmosphere and land components and 1° for the ocean and ice components. NorESM is also available in a lower resolution version (NorESM1-L) and a version that includes prognostic biogeochemical cycling (NorESM1-ME). The latter two model configurations are not part of this paper. Here, a first-order assessment of the model stability, the mean model state and the internal variability based on the model experiments made available to CMIP5 are presented. Further analysis of the model performance is provided in an accompanying paper (Iversen et al., 2013), presenting the corresponding climate response and scenario projections made with NorESM1-M.