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Abstract Large spatio‐temporal gradients in the Congo basin vegetation and rainfall are observed. However, its water‐balance (evapotranspiration minus precipitation, orET − P) is typically measured at basin‐scales, limited primarily by river‐discharge data, spatial resolution of terrestrial water storage measurements, and poorly constrainedET. We use observations of the isotopic composition of water vapor to quantify the spatio‐temporal variability of net surface water fluxes across the Congo Basin between 2003 and 2018. These data are calibrated at basin scale using satellite gravity and total Congo river discharge measurements and then used to estimate time‐varyingET − Pover four quadrants representing the Congo Basin, providing first estimates of this kind for the region. We find that the multi‐year record, seasonality, and interannual variability ofET − Pfrom both the isotopes and the gravity/river discharge based estimates are consistent. Additionally, we use precipitation and gravity‐based estimates with our water vapor isotope‐basedET − Pto calculate time and space averagedETand net river discharge within the Congo Basin. These quadrant‐scale moisture flux estimates indicate (a) substantial recycling of moisture in the Congo Basin (temporally and spatially averagedET/P > 70%), consistent with models and visible light‐basedETestimates, and (b) net river outflow is largest in the Western Congo where there are more rivers and higher flow rates. Our results confirm the importance ofETin modulating the Congo water cycle relative to other water sources. 

Abstract Water stress regulates land‐atmosphere carbon dioxide (CO₂) exchanges in the tropics; however, its role remains poorly characterized due to the confounding roles of radiation, temperature and canopy dynamics. In particular, uncertainty stems from the relative roles of plant‐available water (supply) and atmospheric water vapor deficit (demand) as mechanistic drivers of photosynthetic carbon (C) uptake variability. Using satellite measurements of gravity, CO₂and fluorescence to constrain a mechanistic carbon‐water cycle model from 2001 to 2018, we found that the interannual variability (IAV) of water stress on photosynthetic C uptake was 52% greater than the combined effects of other factors. Surprisingly, the dominance of water stress on C uptake IAV was greater in the wet tropics (94%) than in the dry tropics (26%). Plant‐available water supply and atmospheric demand both contributed to the IAV of water stress on photosynthetic C uptake across the tropics, but the IAV of demand effects was 21% greater than the IAV of supply effects (33% greater in the wet tropics and 6% greater in the dry tropics). We found that the IAV of water stress on C uptake was 24% greater than the IAV of the combination of other factors in the net land‐atmosphere C sink in the whole tropics, 26% greater in the wet tropics, and 7% greater in the dry tropics. Given the recent trends in tropical precipitation and atmospheric humidity, our findings indicate that water stress——from both supply and demand——will likely dominate the climate response of land C sink across tropical ecosystems in the coming decades. 

Abstract Atmospheric humidity and soil moisture in the Amazon forest are tightly coupled to the region’s water balance, or the difference between two moisture fluxes, evapotranspiration minus precipitation (ET-P). However, large and poorly characterized uncertainties in both fluxes, and in their difference, make it challenging to evaluate spatiotemporal variations of water balance and its dependence on ET or P. Here, we show that satellite observations of the HDO/H 2 O ratio of water vapor are sensitive to spatiotemporal variations of ET-P over the Amazon. When calibrated by basin-scale and mass-balance estimates of ET-P derived from terrestrial water storage and river discharge measurements, the isotopic data demonstrate that rainfall controls wet Amazon water balance variability, but ET becomes important in regulating water balance and its variability in the dry Amazon. Changes in the drivers of ET, such as above ground biomass, could therefore have a larger impact on soil moisture and humidity in the dry (southern and eastern) Amazon relative to the wet Amazon. 

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 the 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. 

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, the 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. 

Abstract. We apply airborne measurements across three seasons(summer, winter and spring 2017–2018) in a multi-inversion framework toquantify methane emissions from the US Corn Belt and Upper Midwest, a keyagricultural and wetland source region. Combing our seasonal results withprior fall values we find that wetlands are the largest regional methanesource (32 %, 20 [16–23] Gg/d), while livestock (enteric/manure; 25 %,15 [14–17] Gg/d) are the largest anthropogenic source. Naturalgas/petroleum, waste/landfills, and coal mines collectively make up theremainder. Optimized fluxes improve model agreement with independentdatasets within and beyond the study timeframe. Inversions reveal coherentand seasonally dependent spatial errors in the WetCHARTs ensemble meanwetland emissions, with an underestimate for the Prairie Pothole region butan overestimate for Great Lakes coastal wetlands. Wetland extent andemission temperature dependence have the largest influence on predictionaccuracy; better representation of coupled soil temperature–hydrologyeffects is therefore needed. Our optimized regional livestock emissionsagree well with the Gridded EPA estimates during spring (to within 7 %) butare ∼ 25 % higher during summer and winter. Spatial analysisfurther shows good top-down and bottom-up agreement for beef facilities (withmainly enteric emissions) but larger (∼ 30 %) seasonaldiscrepancies for dairies and hog farms (with > 40 % manureemissions). Findings thus support bottom-up enteric emission estimates butsuggest errors for manure; we propose that the latter reflects inadequatetreatment of management factors including field application. Overall, ourresults confirm the importance of intensive animal agriculture for regionalmethane emissions, implying substantial mitigation opportunities throughimproved management.