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The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.

The increasing complexity of Earth system models has inspired efforts to quantitatively assess model fidelity through rigorous comparison with best available measurements and observational data products. Earth system models exhibit a high degree of spread in predictions of land biogeochemistry, biogeophysics, and hydrology, which are sensitive to forcing from other model components. Based on insights from prior land model evaluation studies and community workshops, the authors developed an open source model benchmarking software package that generates graphical diagnostics and scores model performance in support of the International Land Model Benchmarking (ILAMB) project. Employing a suite of in situ, remote sensing, and reanalysis data sets, the ILAMB package performs comprehensive model assessment across a wide range of land variables and generates a hierarchical set of web pages containing statistical analyses and figures designed to provide the user insights into strengths and weaknesses of multiple models or model versions. Described here is the benchmarking philosophy and mathematical methodology embodied in the most recent implementation of the ILAMB package. Comparison methods unique to a few specific data sets are presented, and guidelines for configuring an ILAMB analysis and interpreting resulting model performance scores are discussed. ILAMB is being adopted by modeling teams and centers during model development and for model intercomparison projects, and community engagement is sought for extending evaluation metrics and adding new observational data sets to the benchmarking framework.

The response of the carbon cycle in prognostic Earth system models (ESMs) contributes significant uncertainty to projections of global climate change. Quantifying contributions of known drivers of interannual variability in the growth rate of atmospheric carbon dioxide (CO2) is important for improving the representation of terrestrial ecosystem processes in these ESMs. Several recent studies have identified the temperature dependence of tropical net ecosystem exchange (NEE) as a primary driver of this variability by analyzing a single, globally averaged time series of CO2 anomalies. Here we examined how the temporal evolution of CO2 in different latitude bands may be used to separate contributions from temperature stress, drought stress, and fire emissions to CO2 variability. We developed atmospheric CO2 patterns from each of these mechanisms during 1997-2011 using an atmospheric transport model. NEE responses to temperature, NEE responses to drought, and fire emissions all contributed significantly to CO2 variability in each latitude band, suggesting that no single mechanism was the dominant driver. We found that the sum of drought and fire contributions to CO2 variability exceeded direct NEE responses to temperature in both the Northern and Southern Hemispheres. Additional sensitivity tests revealed that these contributions are masked by temporal and spatial smoothing of CO2 observations. Accounting for fires, the sensitivity of tropical NEE to temperature stress decreased by 25% to 2.9 ± 0.4 Pg C yr-1 K-1. These results underscore the need for accurate attribution of the drivers of CO2 variability prior to using contemporary observations to constrain long-term ESM responses.

Surface, aircraft, and satellite measurements indicate pervasive early cold season (Augut–September) CO2 emissions across Arctic regions, consistent with increased ecosystem metabolism in plants and soils. A key remaining question is whether cold season sources will become large enough to permanently shift the Arctic into a net carbon source. Polar orbiting GHG satellites provide robust estimation of regional carbon budgets but lack sufficient spatial coverage and repeat frequency to track sink‐to‐source transitions in the early cold season. Mission concepts such as the Arctic Observing Mission (AOM) advocate for flying imaging spectrometers in highly elliptical orbits (HEO) over the Arctic to address sampling limitations. We perform retrieval and flux inversion simulation experiments using the AURORA mission concept, leveraging a Panchromatic imaging Fourier Transform Spectrometer (PanFTS) in HEO. Our simulations demonstrate the potential benefits of increased CO2 sampling for detecting emissions during the early cold season. Plain Language Summary: The Arctic is warming rapidly, leading to an acceleration of carbon exchange across tundra and boreal ecosystems. These changing flows of carbon can be difficult to detect using traditional observing systems. We propose that measurement systems which observe Arctic ecosystems continuously from a quasi‐GEO orbit can more accurately detect these important changes, such that unexpected and dangerous feedbacks to climate warming can be monitored and accounted for. Key Points: The Arctic Fourier Transform Spectrometer Investigation (AURORA) mission concept addresses GHG sampling limitations in the ArcticAURORA uses a highly elliptical orbit (HEO) to increase repeat frequency, and a panchromatic iFTS for SWIR O2 and CO2 bands (0.7–2.5 μm)Simulation experiments show potential benefits of increased sampling frequency for grid scale detection of early cold season CO2 emissions [ABSTRACT FROM AUTHOR]

Changes in atmospheric CO2 variability during the twenty-first century may provide insight about ecosystem responses to climate change and have implications for the design of carbon monitoring programs. This paper describes changes in the three-dimensional structure of atmospheric CO2 for several representative concentration pathways (RCPs 4.5 and 8.5) using the Community Earth System Model–Biogeochemistry (CESM1-BGC). CO2 simulated for the historical period was first compared to surface, aircraft, and column observations. In a second step, the evolution of spatial and temporal gradients during the twenty-first century was examined. The mean annual cycle in atmospheric CO2 was underestimated for the historical period throughout the Northern Hemisphere, suggesting that the growing season net flux in the Community Land Model (the land component of CESM) was too weak. Consistent with weak summer drawdown in Northern Hemisphere high latitudes, simulated CO2 showed correspondingly weak north–south and vertical gradients during the summer. In the simulations of the twenty-first century, CESM predicted increases in the mean annual cycle of atmospheric CO2 and larger horizontal gradients. Not only did the mean north–south gradient increase due to fossil fuel emissions, but east–west contrasts in CO2 also strengthened because of changing patterns in fossil fuel emissions and terrestrial carbon exchange. In the RCP8.5 simulation, where CO2 increased to 1150 ppm by 2100, the CESM predicted increases in interannual variability in the Northern Hemisphere midlatitudes of up to 60% relative to present variability for time series filtered with a 2–10-yr bandpass. Such an increase in variability may impact detection of changing surface fluxes from atmospheric observations.

Interannual variability (IAV) in the atmospheric CO2 growth rate is caused by variation in the balance between uptake by land and ocean and accumulation of anthropogenic emissions in the atmosphere. While variations in terrestrial fluxes are thought to drive most of the observed atmospheric CO2 IAV, the ability to characterize ocean impacts has been limited by the fact that most sites in the surface CO2 monitoring network are located on coasts or islands or within the continental interior. NASA's Orbiting Carbon-Observatory 2 (OCO-2) mission has observed the atmospheric total column carbon dioxide mole fraction (XCO2) from space since September 2014. With a near-global coverage, this dataset provides a first opportunity to directly observe IAV in atmospheric CO2 over remote ocean regions. We assess the impact of ocean flux IAV on the OCO-2 record using atmospheric transport simulations with underlying gridded air-sea CO2 fluxes from observation-based products. We use three observation-based products to bracket the likely range of ocean air-sea flux contributions to XCO2 variability (over both land and ocean) within the GEOS-Chem atmospheric transport model. We find that the magnitude of XCO2 IAV generated by the whole ocean is between 0.08-0.12 ppm throughout the world. Depending on location and flux product, between 20-80% of the IAV in the simulations is caused by IAV in air-sea CO2 fluxes, with the remainder due to IAV in atmospheric winds, which modulate the atmospheric gradients that arise from climatological ocean fluxes. The Southern Hemisphere mid-latitudes and low-latitudes are the dominant ocean regions in generating the XCO2 IAV globally. The simulation results based on all three flux products show that even within the Northern Hemisphere atmosphere, Southern Hemisphere ocean fluxes are the dominant source of variability in XCO2. Nevertheless, the small magnitude of the air-sea flux impacts on XCO2 presents a substantial challenge for detection of oceandriven IAV from OCO-2. Although the IAV amplitude arising from ocean fluxes and transport is 20 to 50% of the total observed XCO2 IAV amplitude of 0.4 to 1.6 ppm in the Southern Hemisphere and the tropics, ocean-driven IAV represents only 10% of the observed amplitude in the Northern Hemisphere. We find that for all three products, the simulated ocean-driven XCO2 IAV is weakly anticorrelated with OCO-2 observations, although these correlations are not statistically significant (p>0.05), suggesting that even over ocean basins, terrestrial IAV obscures the ocean signal. [ABSTRACT FROM AUTHOR]

Variations in atmosphere total column‐mean CO2 (XCO2) collected by the National Aeronautics and Space Administration's Orbiting Carbon Observatory‐2 satellite can be used to constrain surface carbon fluxes if the influence of atmospheric transport and observation errors on the data is known and accounted for. Due to sparse validation data, the portions of fine‐scale variability in XCO2 driven by fluxes, transport, or retrieval errors remain uncertain, particularly over the ocean. To better understand these drivers, we characterize variability in OCO‐2 Level 2 version 10 XCO2 from the seasonal scale, synoptic‐scale (order of days, thousands of kilometers), and mesoscale (within‐day, hundreds of kilometers) for 10 biomes over North America and adjacent ocean basins. Seasonal and synoptic variations in XCO2 reflect real geophysical drivers (transport and fluxes), following large‐scale atmospheric circulation and the north‐south distribution of biosphere carbon uptake. In contrast, geostatistical analysis of mesoscale and finer variability shows that real signals are obscured by systematic biases across the domain. Spatial correlations in along‐track XCO2 are much shorter and spatially coherent variability is much larger in magnitude than can be attributed to fluxes or transport. We characterize random and coherent along‐track XCO2 variability in addition to quantifying uncertainty in XCO2 aggregates across typical lengths used in inverse modeling. Even over the ocean, correlated errors decrease the independence and increase uncertainty in XCO2. We discuss the utility of computing geostatistical parameters and demonstrate their importance for XCO2 science applications spanning from data reprocessing and algorithm development to error estimation and carbon flux inference. Plain Language Summary: The National Aeronautics and Space Administration's Orbiting Carbon Observatory‐2 satellite collects measurements of atmosphere total column‐mean CO2 (XCO2), providing a constraint on surface carbon fluxes. Fluxes of carbon into Earth's surface by the ocean and land biosphere (uptake) counteract the rising levels of atmospheric CO2 caused by increased anthropogenic emissions. To use XCO2 for flux estimation in inverse models, variability in the data must be attributed to either gradients in surface carbon fluxes, atmospheric transport, or retrieval errors. We decompose OCO‐2 XCO2 variability over North America and adjacent ocean into seasonal, synoptic (order of days, thousands of kilometers) and finer scales to uncover the relative influences of these processes on XCO2. Spatial patterns in seasonal and synoptic‐scale XCO2 variability follow large‐scale atmospheric circulation and reflect the mean north‐south distribution of biosphere carbon uptake in the Northern Hemisphere rather than underlying local surface flux variability. On finer scales, geostatistical analysis shows that patterns in XCO2 variability are driven by correlated retrieval errors, obscuring the influence of transport and error. We compute new estimates of XCO2 uncertainty for inverse model studies that assimilate the data and discuss the impact of errors over different land and ocean regions. Key Points: We attribute variability in XCO2 retrieved from NASA's OCO‐2 satellite to surface flux gradients, atmospheric transport, and errorSeasonal and synoptic‐scale XCO2 variability reflects hemispheric and continental‐scale surface carbon flux gradientsCorrelated errors impart spatially coherent fine‐scale variability that significantly increases standard error in XCO2 aggregates [ABSTRACT FROM AUTHOR]

Atmospheric carbon dioxide (CO2) accounts for the largest radiative forcing among anthropogenic greenhouse gases. There is, therefore, a pressing need to understand the rate at which CO2 accumulates in the atmosphere, including the interannual variations (IAV) in this rate. IAV in the CO2 growth rate is a small signal relative to the long-term trend and the mean annual cycle of atmospheric CO2, and IAV is tied to climatic variations that may provide insights into long-term carbon-climate feedbacks. Observations from the Orbiting Carbon Observatory-2 (OCO-2) mission offer a new opportunity to refine our understanding of atmospheric CO2 IAV since the satellite can measure over remote terrestrial regions and the open ocean where traditional in situ CO2 monitoring is difficult. In this study, we analyze the IAV of column-averaged dry air CO2 mole fraction (XCO2) from OCO-2 between September 2014 to June 2021. The amplitude of IAV variations is up to 1.2 ppm over the continents and around 0.4 ppm over the open ocean. Across all latitudes, the OCO-2 detected XCO2 IAV shows a clear relationship with ENSO-driven variations that originate in the tropics and are transported poleward. The XCO2 IAV timeseries shows similar zonal patterns compared to ground-based in situ observations and with column observations from the Total Carbon Column Observing Network (TCCON). At lower degrees of aggregation (i.e., 5°x5° grid cells), there are larger inconsistencies with TCCON suggesting that one or both of the observing systems are affected by bias or systematic retrieval issues that are of a similar magnitude to the IAV signal. Our results suggest that OCO-2 IAV provides meaningful information about climate-driven variations in carbon fluxes and provides new opportunities to monitor climate-driven variations in CO2 over open ocean and remote regions. [ABSTRACT FROM AUTHOR]

We present a method to reduce the impact of source brightness fluctuations (SBFs) on spectra recorded by Fourier-transform spectrometry (FTS). Interferograms are recorded without AC coupling of the detector signal (DC mode). The SBF are determined by low-pass filtering of the DC interferograms, which are then reweighted by the low-pass, smoothed signal. Atmospheric solar absorption interferograms recorded in DC mode have been processed with and without this technique, and we demonstrate its efficacy in producing more consistent retrievals of atmospheric composition. We show that the reweighting algorithm improves retrievals from interferograms subject to both gray and nongray intensity fluctuations, making the algorithm applicable to atmospheric data contaminated by significant amounts of aerosol or cloud cover.