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Recent advances in satellite observations of solar-induced chlorophyll fluorescence (SIF) provide a new opportunity to constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and its radiative transfer to remote sensing sensors is an essential prerequisite for data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf-level to canopy-level is usually not well-represented. Here, we incorporate the simulation of far-red SIF observed at nadir into the Community Land Model version 5 (CLM5). Leaf-level fluorescence yield was simulated by a parametric simplification of the Soil Canopy-Observation of Photosynthesis and Energy fluxes model (SCOPE). And an efficient and accurate method based on escape probability is developed to scale SIF from leaf-level to top-of-canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and SCOPE agreed well at sites except one in needleleaf forest (R 2 > 0.91, root-mean-square error  0.68). At the global scale, simulated SIF generally captured the spatial and seasonal patterns of satellite-observed SIF. Factors including the fluorescence emission model, clumping, bidirectional effect, and leaf optical properties had considerable impacts on SIF simulation, and the discrepancies between simulate d and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF toward constraining GPP simulations.

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901-2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

Terrestrial photosynthesis is regulated by plant phenology and environmental conditions, both of which experienced substantial changes in recent decades. Unlike early-season photosynthesis, which is mostly driven by temperature or wet-season onset, late-season photosynthesis can be limited by several factors and the underlying mechanisms are less understood. Here, we analyze the temperature and water limitations on the ending date of photosynthesis (EOP), using data from both remote-sensing and flux tower-based measurements. We find a contrasting spatial pattern of temperature and water limitations on EOP. The threshold separating these is determined by the balance between energy availability and soil water supply. This coordinated temperature and moisture regulation can be explained by "law of minimum," i.e., as temperature limitation diminishes, higher soil water is needed to support increased vegetation activity, especially during the late growing season. Models project future warming and drying, especially during late season, both of which should further expand the water-limited regions, causing large variations and potential decreases in photosynthesis.

Northern hemisphere evergreen forests assimilate a significant fraction of global atmospheric CO2 but monitoring large-scale changes in gross primary production (GPP) in these systems is challenging. Recent advances in remote sensing allow the detection of solar-induced chlorophyll fluorescence (SIF) emission from vegetation, which has been empirically linked to GPP at large spatial scales. This is particularly important in evergreen forests, where traditional remote-sensing techniques and terrestrial biosphere models fail to reproduce the seasonality of GPP. Here, we examined the mechanistic relationship between SIF retrieved from a canopy spectrometer system and GPP at a winter-dormant conifer forest, which has little seasonal variation in canopy structure, needle chlorophyll content, and absorbed light. Both SIF and GPP track each other in a consistent, dynamic fashion in response to environmental conditions. SIF and GPP are well correlated (R 2 = 0.62-0.92) with an invariant slope over hourly to weekly timescales. Large seasonal variations in SIF yield capture changes in photoprotective pigments and photosystem II operating efficiency associated with winter acclimation, highlighting its unique ability to precisely track the seasonality of photosynthesis. Our results underscore the potential of new satellite-based SIF products (TROPOMI, OCO-2) as proxies for the timing and magnitude of GPP in evergreen forests at an unprecedented spatiotemporal resolution.

The ability to accurately predict ecosystem drought response and recovery is necessary to produce reliable forecasts of land carbon uptake and future climate. Using a suite of models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we assessed modeled net primary productivity (NPP) response to, and recovery from, drought events against a benchmark derived from tree ring observations between 1948 and 2008 across forested regions of the US and Europe. We find short lag times (0-6 months) between climate anomalies and modeled NPP response. Although models accurately simulate the direction of drought legacy effects (i.e. NPP decreases), projected effects are approximately four times shorter and four times weaker than observations suggest. This discrepancy between observed and simulated vegetation recovery from drought reveals a potential critical model deficiency. Since productivity is a crucial component of the land carbon balance, models that underestimate drought recovery time could overestimate predictions of future land carbon sink strength and, consequently, underestimate forecasts of atmospheric CO2.

Thaw and release of permafrost carbon (C) due to climate change is likely to offset increased vegetation C uptake in northern high-latitude (NHL) terrestrial ecosystems. Models project that this permafrost C feedback may act as a slow leak, in which case detection and attribution of the feedback may be difficult. The formation of talik, a subsurface layer of perennially thawed soil, can accelerate permafrost degradation and soil respiration, ultimately shifting the C balance of permafrost-affected ecosystems from long-term C sinks to long-term C sources. It is imperative to understand and characterize mechanistic links between talik, permafrost thaw, and respiration of deep soil C to detect and quantify the permafrost C feedback. Here, we use the Community Land Model (CLM) version 4.5, a permafrost and biogeochemistry model, in comparison to long-term deep borehole data along North American and Siberian transects, to investigate thaw-driven C sources in NHL ( > 55° N) from 2000 to 2300. Widespread talik at depth is projected across most of the NHL permafrost region (14 millionĝ€†km2) by 2300, 6.2 millionĝ€†km2 of which is projected to become a long-term C source, emitting 10 Pgĝ€†C by 2100, 50 Pgĝ€†C by 2200, and 120 Pgĝ€†C by 2300, with few signs of slowing. Roughly half of the projected C source region is in predominantly warm sub-Arctic permafrost following talik onset. This region emits only 20 Pgĝ€†C by 2300, but the CLM4.5 estimate may be biased low by not accounting for deep C in yedoma. Accelerated decomposition of deep soil C following talik onset shifts the ecosystem C balance away from surface dominant processes (photosynthesis and litter respiration), but sink-to-source transition dates are delayed by 20-200 years by high ecosystem productivity, such that talik peaks early ( ĝ1/4 2050s, although borehole data suggest sooner) and C source transition peaks late ( ĝ1/4 2150-2200). The remaining C source region in cold northern Arctic permafrost, which shifts to a net source early (late 21st century), emits 5 times more C (95 Pgĝ€†C) by 2300, and prior to talik formation due to the high decomposition rates of shallow, young C in organic-rich soils coupled with low productivity. Our results provide important clues signaling imminent talik onset and C source transition, including (1) late cold-season (January-February) soil warming at depth ( ĝ1/4 2 m), (2) increasing cold-season emissions (November-April), and (3) enhanced respiration of deep, old C in warm permafrost and young, shallow C in organic-rich cold permafrost soils. Our results suggest a mosaic of processes that govern carbon source-to-sink transitions at high latitudes and emphasize the urgency of monitoring soil thermal profiles, organic C age and content, cold-season CO2 emissions, and atmospheric 14CO2 as key indicators of the permafrost C feedback.

High-latitude ecosystems have the capacity to release large amounts of carbon dioxide (CO2) to the atmosphere in response to increasing temperatures, representing a potentially significant positive feedback within the climate system. Here, we combine aircraft and tower observations of atmospheric CO2 with remote sensing data and meteorological products to derive temporally and spatially resolved year-round CO2 fluxes across Alaska during 2012-2014. We find that tundra ecosystems were a net source of CO2 to the atmosphere annually, with especially high rates of respiration during early winter (October through December). Long-term records at Barrow, AK, suggest that CO2 emission rates from North Slope tundra have increased during the October through December period by 73% ± 11% since 1975, and are correlated with rising summer temperatures. Together, these results imply increasing early winter respiration and net annual emission of CO2 in Alaska, in response to climate warming. Our results provide evidence that the decadal-scale increase in the amplitude of the CO2 seasonal cycle may be linked with increasing biogenic emissions in the Arctic, following the growing season. Early winter respiration was not well simulated by the Earth System Models used to forecast future carbon fluxes in recent climate assessments. Therefore, these assessments may underestimate the carbon release from Arctic soils in response to a warming climate.

With rapid changes in climate and the seasonal amplitude of carbon dioxide (CO2) in the Arctic, it is critical that we detect and quantify the underlying processes controlling the changing amplitude of CO2 to better predict carbon cycle feedbacks in the Arctic climate system. We use satellite and airborne observations of atmospheric CO2 with climatically forced CO2 flux simulations to assess the detectability of Alaskan carbon cycle signals as future warming evolves. We find that current satellite remote sensing technologies can detect changing uptake accurately during the growing season but lack sufficient cold season coverage and near-surface sensitivity to constrain annual carbon balance changes at regional scale. Airborne strategies that target regular vertical profile measurements within continental interiors are more sensitive to regional flux deeper into the cold season but currently lack sufficient spatial coverage throughout the entire cold season. Thus, the current CO2 observing network is unlikely to detect potentially large CO2 sources associated with deep permafrost thaw and cold season respiration expected over the next 50 y. Although continuity of current observations is vital, strategies and technologies focused on cold season measurements (active remote sensing, aircraft, and tall towers) and systematic sampling of vertical profiles across continental interiors over the full annual cycle are required to detect the onset of carbon release from thawing permafrost.

Climate extremes such as drought and heat waves can cause substantial reductions in terrestrial carbon uptake. Advancing projections of the carbon uptake response to future climate extremes depends on (1) identifying mechanistic links between the carbon cycle and atmospheric drivers, (2) detecting and attributing uptake changes, and (3) evaluating models of land response and atmospheric forcing. Here, we combine model simulations, remote sensing products, and ground observations to investigate the impact of climate variability on carbon uptake in the Texas-northern Mexico region. Specifically, we (1) examine the relationship between drought, carbon uptake, and variability of El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) using the Joint UK Land-Environment Simulator (JULES) biosphere simulations from 1950-2012, (2) quantify changes in carbon uptake during record drought conditions in 2011, and (3) evaluate JULES carbon uptake and soil moisture in 2011 using observations from remote sensing and a network of flux towers in the region. Long-term simulations reveal systematic decreases in regional-scale carbon uptake during negative phases of ENSO and NAO, including amplified reductions of gross primary production (GPP) (-0.42 ± 0.18 Pg C yr-1) and net ecosystem production (NEP) (-0.14 ± 0.11 Pg C yr-1) during strong La Niña years. The 2011 megadrought caused some of the largest declines of GPP (-0.50 Pg C yr-1) and NEP (-0.23 Pg C yr-1) in our simulations. In 2011, consistent declines were found in observations, including high correlation of GPP and surface soil moisture (r = 0.82 ± 0.23, p = 0.012) in remote sensing-based products. These results suggest a large-scale response of carbon uptake to ENSO and NAO, and highlight a need to improve model predictions of ENSO and NAO in order to improve predictions of future impacts on the carbon cycle and the associated feedbacks to climate change.

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]

Boreal‐Arctic regions are key stores of organic carbon (C) and play a major role in the greenhouse gas balance of high‐latitude ecosystems. The carbon‐climate (C‐climate) feedback potential of northern high‐latitude ecosystems remains poorly understood due to uncertainty in temperature and precipitation controls on carbon dioxide (CO2) uptake and the decomposition of soil C into CO2 and methane (CH4) fluxes. While CH4 fluxes account for a smaller component of the C balance, the climatic impact of CH4 outweighs CO2 (28–34 times larger global warming potential on a 100‐year scale), highlighting the need to jointly resolve the climatic sensitivities of both CO2 and CH4. Here, we jointly constrain a terrestrial biosphere model with in situ CO2 and CH4 flux observations at seven eddy covariance sites using a data‐model integration approach to resolve the integrated environmental controls on land‐atmosphere CO2 and CH4 exchanges in Alaska. Based on the combined CO2 and CH4 flux responses to climate variables, we find that 1970‐present climate trends will induce positive C‐climate feedback at all tundra sites, and negative C‐climate feedback at the boreal and shrub fen sites. The positive C‐climate feedback at the tundra sites is predominantly driven by increased CH4 emissions while the negative C‐climate feedback at the boreal site is predominantly driven by increased CO2 uptake (80% from decreased heterotrophic respiration, and 20% from increased photosynthesis). Our study demonstrates the need for joint observational constraints on CO2 and CH4 biogeochemical processes—and their associated climatic sensitivities—for resolving the sign and magnitude of high‐latitude ecosystem C‐climate feedback in the coming decades. Key Points: We use in situ measurements to constrain the modeled joint climatic sensitivity of land‐atmosphere CH4 and CO2 exchangesA continued 1970‐present climate trend leads to positive C‐climate feedback in wet tundra sites but negative feedback in boreal and shrub fen sitesCH4 respiration dominates the positive tundra site feedback, CO2 respiration dominates the negative boreal and shrub fen sites feedback [ABSTRACT FROM AUTHOR]

Terrestrial carbon cycle models are routinely used to determine the response of the land carbon sink under expected future climate change, yet these predictions remain highly uncertain. Increasing the realism of processes in these models may help with predictive skill, but any such addition should be confronted with observations and evaluated in the context of the aggregate behavior of the carbon cycle. Here, two formulations for leaf area index (LAI) phenology are coupled to the same terrestrial biosphere model: one is climate agnostic, and the other incorporates direct environmental controls on both timing and growth. Each model is calibrated simultaneously to observations of LAI, net ecosystem exchange (NEE), and biomass using the CARbon DAta-MOdel fraMework (CARDAMOM) and validated against withheld data, including eddy covariance estimates of gross primary productivity (GPP) and ecosystem respiration (Re) across six ecosystems from the tropics to high latitudes. Both model formulations show similar predictive skill for LAI and NEE. However, with the addition of direct environmental controls on LAI, the integrated model explains 22 % more variability in GPP and Re and reduces biases in these fluxes by 58 % and 77 %, respectively, while also predicting more realistic annual litterfall rates due to changes in carbon allocation and turnover. We extend this analysis to evaluate the inferred climate sensitivity of LAI and NEE with the new model and show that the added complexity shifts the sign, magnitude, and seasonality of NEE sensitivity to precipitation and temperature. This highlights the benefit of process complexity when inferring underlying processes from Earth observations and representing the climate response of the terrestrial carbon cycle. [ABSTRACT FROM AUTHOR]

Terrestrial carbon cycle models are routinely used to determine the response of the land carbon sink under expected future climate change, yet these predictions remain highly uncertain. Increasing the realism of processes in these models may help with predictive skill, but any such addition should be confronted with observations and evaluated in the context of the aggregate behavior of the carbon cycle. Here, two formulations for leaf area index (LAI) phenology are coupled to the same terrestrial biosphere model, one is climate agnostic and the other incorporates direct environmental controls on both timing and growth. Each model is calibrated simultaneously to observations of LAI, net ecosystem exchange (NEE), and biomass using the CARbon DAta-MOdel fraMework (CARDAMOM), and validated against withheld data including eddy covariance estimates of gross primary productivity (GPP) and ecosystem respiration (Re), across six ecosystems from the tropics to high-latitudes. Both model formulations show similar predictive skill for LAI and NEE. However, with the addition of direct environmental controls on LAI, the integrated model explains 22 % more variability in GPP and Re, and reduces biases in these fluxes by 58 % and 77 %, respectively, while also predicting more realistic annual litterfall rates, due to changes in carbon allocation and turnover. We extend this analysis to evaluate the inferred climate sensitivity of LAI and NEE with the new model, and show that the added complexity shifts the sign, magnitude, and seasonality of NEE sensitivity to precipitation and temperature. This highlights the benefit of process complexity when inferring underlying processes from Earth observations and in representing the climate response of the terrestrial carbon cycle. [ABSTRACT FROM AUTHOR]