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The new generation of heterogeneous CPU/GPU computer systems offer much greater computational performance but are not yet widely used for climate modeling. One reason for this is that traditional climate models were written before GPUs were available and would require an extensive overhaul to run on these new machines. In addition, even conventional “high–resolution” simulations don't currently provide enough parallel work to keep GPUs busy, so the benefits of such overhaul would be limited for the types of simulations climate scientists are accustomed to. The vision of the Simple Cloud‐Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) project is to create a global atmospheric model with the architecture to efficiently use GPUs and horizontal resolution sufficient to fully take advantage of GPU parallelism. After 5 years of model development, SCREAM is finally ready for use. In this paper, we describe the design of this new code, its performance on both CPU and heterogeneous machines, and its ability to simulate real‐world climate via a set of four 40 day simulations covering all 4 seasons of the year. Plain Language Summary This paper describes the design and development of a 3 km version of the Energy Exascale Earth System Model (E3SM) atmosphere model, which has been fully rewritten in C++ using the Kokkos library for performance portability. This newly rewritten model is able to take advantage of the state–of–the–science high performance computing systems which use graphical processor units (GPUs) to mitigate much of the computational expense which typically plagues high–resolution global modeling. Taking advantage of this high–performance we are able to run four seasons of simulations at 3 km global resolution. We discuss the biases, including the diurnal cycle, by comparing model results with satellite and Atmospheric Radiation Measurement ground‐based site data. Key Points Describes the C++/Kokkos implementation of the Simple Cloud–Resolving E3SM Atmosphere Model (SCREAMv1) SCREAMv1 leverages GPUs to surpass one simulated year per compute day at global 3 km resolution High resolution improves some meso‐scale features and the diurnal cycle but large‐scale biases require improvement across all four seasons

The CALIOP (Cloud‐Aerosol Lidar with Orthogonal Polarization) layer product is used for a multimodel evaluation of the vertical distribution of aerosols. Annual and seasonal aerosol extinction profiles are analyzed over 13 sub‐continental regions representative of industrial, dust, and biomass burning pollution, from CALIOP 2007–2009 observations and from AeroCom (Aerosol Comparisons between Observations and Models) 2000 simulations. An extinction mean height diagnostic (Zα) is defined to quantitatively assess the models' performance. It is calculated over the 0–6 km and 0–10 km altitude ranges by weighting the altitude of each 100 m altitude layer by its aerosol extinction coefficient. The mean extinction profiles derived from CALIOP layer products provide consistent regional and seasonal specificities and a low inter‐annual variability. While the outputs from most models are significantly correlated with the observed Zα climatologies, some do better than others, and 2 of the 12 models perform particularly well in all seasons. Over industrial and maritime regions, most models show higher Zα than observed by CALIOP, whereas over the African and Chinese dust source regions, Zα is underestimated during Northern Hemisphere Spring and Summer. The positive model bias in Zα is mainly due to an overestimate of the extinction above 6 km. Potential CALIOP and model limitations, and methodological factors that might contribute to the differences are discussed. Key Points Mean regional tropospheric aerosol extinction profiles are calculated from CALIOP data. An extinction mean height diagnostic is defined. The performance of 12 global models in simulating the aerosol profiles is evaluated.

The world’s coastal areas are increasingly at risk of coastal flooding due to sea-level rise. We present a novel global dataset of extreme sea levels, the Coastal Dataset for the Evaluation of Climate Impact (CoDEC), which can be used to accurately map the impact of climate change on coastal regions around the world. The third generation Global Tide and Surge Model, with a coastal resolution of 2.5 km (1.25 km in Europe), was used to simulate extreme sea levels for the ERA5 climate reanalysis from 1979 to 2017, as well as for future climate scenarios from 2040 to 2100. The validation against observed sea levels demonstrated a good performance, and the annual maxima had a mean bias of -0.04 m, which is 50% lower than the mean bias of the previous GTSR dataset. By the end of the century (2071-2100), it is projected that the 1 in 10-year water levels will have increased 0.34 m on average for RCP4.5, while some locations may experience increases of up to 0.5 m. The change in return levels is largely driven by sea-level rise, although at some locations changes in storms surges and interaction with tides amplify the impact of sea-level rise with changes up to 0.2 m. By presenting an application of the CoDEC dataset to the city of Copenhagen, we demonstrate how climate impact indicators derived from simulation can contribute to an understanding of climate impact on a local scale. Moreover, the CoDEC output locations are designed to be used as boundary conditions for regional models, and we envisage that they will be used for dynamic downscaling.

We present a simple theoretical land‐surface classification that can be used to determine the location and temporal behavior of preferential sources of terrestrial dust emissions. The classification also provides information about the likely nature of the sediments, their erodibility and the likelihood that they will generate emissions under given conditions. The scheme is based on the dual notions of geomorphic type and connectivity between geomorphic units. We demonstrate that the scheme can be used to map potential modern‐day dust sources in the Chihuahuan Desert, the Lake Eyre Basin and the Taklamakan. Through comparison with observed dust emissions, we show that the scheme provides a reasonable prediction of areas of emission in the Chihuahuan Desert and in the Lake Eyre Basin. The classification is also applied to point source data from the Western Sahara to enable comparison of the relative importance of different land surfaces for dust emissions. We indicate how the scheme could be used to provide an improved characterization of preferential dust sources in global dust‐cycle models. Key Points A geomorphological classification is used to assess dust emission potential Emission intensity is based on soil texture and likely temporal variability The scheme may improve dust sources delimitation in global dust‐cycle models

The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (approximately biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modeling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations).For the 2003-2012 decade, global methane emissions are estimated by top-down inversions at 558 TgCH4 yr(exp -1), range 540-568. About 60 of global emissions are anthropogenic (range 50-65%). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scenarios. Bottom-up approaches suggest larger global emissions (736 TgCH4 yr(exp -1), range 596-884) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the top-down budget, it is likely that some of the individual emissions reported by the bottom-up approaches are overestimated, leading to too large global emissions. Latitudinal data from top-down emissions indicate a predominance of tropical emissions (approximately 64% of the global budget, less than 30deg N) as compared to mid (approximately 32%, 30-60deg N) and high northern latitudes (approximately 4%, 60-90deg N). Top-down inversions consistently infer lower emissions in China (approximately 58 TgCH4 yr(exp -1), range 51-72, minus14% ) and higher emissions in Africa (86 TgCH4 yr(exp -1), range 73-108, plus 19% ) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40% on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (http://doi.org/10.3334/CDIAC/GLOBAL_ METHANE_BUDGET_2016_V1.1) and the Global Carbon Project.

We derive an implicit-explicit (IMEX) formalism for the three-dimensional (3D) Euler equations that allow a unified representation of various nonhydrostatic flow regimes, including cloud resolving and mesoscale (flow in a 3D Cartesian domain) as well as global regimes (flow in spherical geometries). This general IMEX formalism admits numerous types of methods including single-stage multistep methods (e.g., Adams methods and backward difference formulas) and multistage single-step methods (e.g., additive Runge--Kutta methods). The significance of this result is that it allows a numerical model to reuse the same machinery for all classes of time-integration methods described in this work. We also derive two classes of IMEX methods, one-dimensional and 3D, and show that they achieve their expected theoretical rates of convergence regardless of the geometry (e.g., 3D box or sphere) and introduce a new second-order IMEX Runge--Kutta method that performs better than the other second-order methods considered. We then compare all the IMEX methods in terms of accuracy and efficiency for two types of geophysical fluid dynamics problems: buoyant convection and inertia-gravity waves. These results show that the high-order time-integration methods yield better efficiency particularly when high levels of accuracy are desired. [PUBLICATION ABSTRACT]

Global simulations of sulfate, nitrate, and ammonium aerosols are performed for the present day and 2050 using the chemical transport model GEOS‐Chem. Changes in climate and emissions projected by the IPCC A1B scenario are imposed separately and together, with the primary focus of the work on future inorganic aerosol levels over the United States. Climate change alone is predicted to lead to decreases in levels of sulfate and ammonium in the southeast U.S. but increases in the Midwest and northeast U.S. Nitrate concentrations are projected to decrease across the U.S. as a result of climate change alone. In the U.S., climate change alone can cause changes in annually averaged sulfate‐nitrate‐ammonium of up to 0.61 μg/m3, with seasonal changes often being much larger in magnitude. When changes in anthropogenic emissions are considered (with or without changes in climate), domestic sulfate concentrations are projected to decrease because of sulfur dioxide emission reductions, and nitrate concentrations are predicted to generally increase because of higher ammonia emissions combined with decreases in sulfate despite reductions in emissions of nitrogen oxides. The ammonium burden is projected to increase from 0.24 to 0.36 Tg, and the sulfate burden to increase from 0.28 to 0.40 Tg S as a result of globally higher ammonia and sulfate emissions in the future. The global nitrate burden is predicted to remain essentially constant at 0.35 Tg, with changes in both emissions and climate as a result of the competing effects of higher precursor emissions and increased temperature.

The tropical cyclone (TC) forecast skill of the eight global medium‐range forecast models which are participating in the DIfferent Models, Same Initial Conditions project is investigated in this study. Each model was used to generate 10‐day forecasts from the same initial conditions provided by the European Centre for Medium‐Range Weather Forecasts. There are a total of 123 initial dates spanning in one year from June 2018 to June 2019 at 3‐day intervals. The TC track and intensity forecasts are evaluated against the best track data set. TC‐related precipitation and tropical cyclogenesis forecasts are also compared to explore the differences and similarities of TC forecasts across the models. This comparison of TC forecasts allows model developers in different centers to benchmark their model against other models, with the impact of the initial condition quality removed. The verifications reveal that most models show slow‐moving and right‐of‐track biases in their TC track forecasts. Also, a common dry bias in TC‐related precipitation indicates a general deficiency in TC intensity and convection in the models which should be related to insufficient model resolution. These findings provide important references for future model developments. Plain Language Summary Despite recent improvements in our ability to predict the track and intensity of tropical cyclones (TCs), these storms remain significant forecasting challenges. Forecasters rely heavily on the guidance generated by numerical weather prediction systems, making the reliability of these systems essential for accurate forecasts during these high‐impact weather events. As a result, improvement in the quality of tropical cyclone guidance is an important objective for numerical model development. In this study, the TC forecast skill in the eight global medium‐range forecast models from the model development centers/institutes who participated in the DIfferent Models, Same Initial Conditions project are examined. All models were initialized from the same data provided by European Centre for Medium‐Range Weather Forecasts to investigate the differences and similarities among their respective TC forecasts without the impact of the quality of initial conditions. Besides the general TC forecast evaluation metrics including errors and biases of the track and intensity, the TC‐related precipitation and TC genesis skills are also evaluated to comprehensively explore the performance of TC forecasts among all models. The comparison allows model developers in different centers to benchmark their model against other participating models. Moreover, the verification results provide important references for future model developments. Key Points Tropical cyclone forecasts are compared between global medium‐range models from leading modeling centers initialized with identical data Similarities and differences between the models set a benchmark of TC forecast with the impact of the initial condition quality removed Common TC forecast biases indicate general deficiencies in the models and suggest a direction for further model improvement