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  1. Abstract. The flow of carbon through terrestrial ecosystems and the response toclimate are critical but highly uncertain processes in the global carboncycle. However, with a rapidly expanding array of in situ and satellitedata, there is an opportunity to improve our mechanistic understanding ofthe carbon (C) cycle's response to land use and climate change. Uncertaintyin temperature limitation on productivity poses a significant challenge topredicting the response of ecosystem carbon fluxes to a changing climate.Here we diagnose and quantitatively resolve environmental limitations onthe growing-season onset of gross primary production (GPP) using nearly 2 decades of meteorological and C flux data (2000–2018) at a subalpineevergreen forest in Colorado, USA. We implement the CARbonDAta-MOdel fraMework (CARDAMOM) model–datafusion network to resolve the temperature sensitivity of spring GPP. Tocapture a GPP temperature limitation – a critical component of the integratedsensitivity of GPP to temperature – we introduced a cold-temperature scalingfunction in CARDAMOM to regulate photosynthetic productivity. We found thatGPP was gradually inhibited at temperatures below 6.0 ∘C (±2.6 ∘C) and completely inhibited below −7.1 ∘C(±1.1 ∘C). The addition of this scaling factor improvedthe model's ability to replicate spring GPP at interannual and decadal timescales (r=0.88), relative to the nominal CARDAMOM configuration (r=0.47), and improved spring GPP model predictability outside of themore »dataassimilation training period (r=0.88). While cold-temperaturelimitation has an important influence on spring GPP, it does not have asignificant impact on integrated growing-season GPP, revealing that otherenvironmental controls, such as precipitation, play a more important role inannual productivity. This study highlights growing-season onset temperatureas a key limiting factor for spring growth in winter-dormant evergreenforests, which is critical in understanding future responses to climatechange.« less
  2. Abstract. We introduce a transformed isentropic coordinate Mθe,defined as the dry air mass under a given equivalent potential temperaturesurface (θe) within a hemisphere. Like θe, thecoordinate Mθe follows the synoptic distortions of theatmosphere but, unlike θe, has a nearly fixedrelationship with latitude and altitude over the seasonal cycle. Calculationof Mθe is straightforward from meteorological fields. Usingobservations from the recent HIAPER Pole-to-Pole Observations (HIPPO) and Atmospheric Tomography Mission (ATom) airborne campaigns, we map theCO2 seasonal cycle as a function of pressure and Mθe, whereMθe is thereby effectively used as an alternative tolatitude. We show that the CO2 seasonal cycles are more constantas a function of pressure using Mθe as the horizontal coordinatecompared to latitude. Furthermore, short-term variability inCO2 relative to the mean seasonal cycle is also smaller when the dataare organized by Mθe and pressure than when organized by latitudeand pressure. We also present a method using Mθe to computemass-weighted averages of CO2 on a hemispheric scale. Using this methodwith the same airborne data and applying corrections for limited coverage,we resolve the average CO2 seasonal cycle in the Northern Hemisphere(mass-weighted tropospheric climatological average for 2009–2018), yieldingan amplitude of 7.8 ± 0.14 ppm and a downward zero-crossing on Julianday 173 ± 6.1 (i.e., late June). Mθe may be similarlyusefulmore »for mapping the distribution and computing inventories of anylong-lived chemical tracer.« less
  3. Abstract. The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at six globally distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, themore »complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.« less