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Abstract The Congo basin hosts one of the largest terrestrial precipitation centers. Yet, the mechanisms that start the rainy seasons in Congo have not been studied systematically. We show that the transition from the dry to the rainy season over the southern Congo is initiated by a decrease in moisture export towards the Sahel, about three to four months before the rainy season onset (RSO), referred to as the pre-transition period. During this period, evapotranspiration (ET) is low due to low surface solar radiation, resulting from low insolation and high amounts of low-level clouds. In the early transition period, one to three months before the RSO, column water vapor increases due to increased oceanic moisture transport. Meanwhile, ET starts increasing due to increases in surface radiation and vegetation photosynthesis, despite a lack of soil moisture increases. Finally, in the late transition period, about one month before the RSO, ET continues to increase, contributing equally to atmospheric moisture needed for deep convection as advected oceanic moisture. Additionally, the formation of the African Easterly-Jet South and the southward movement of the Congo Air Boundary increase vertical wind shear and provide large-scale dynamic lifting of the warm and humid air from Congo. The frequency of deep convection increases rapidly, leading to the start of the rainy season. Therefore, the RSO over southern-hemispheric Congo basin is a result of combined large-scale atmospheric circulation change driven by increasing land–ocean surface temperature gradient and vegetation response to the seasonal change of insolation. 

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 The rapid increase in the volume and variety of terrestrial biosphere observations (i.e., remote sensing data and in situ measurements) offers a unique opportunity to derive ecological insights, refine process‐based models, and improve forecasting for decision support. However, despite their potential, ecological observations have primarily been used to benchmark process‐based models, as many past and current models lack the capability to directly integrate observations and their associated uncertainties for parameterization. In contrast, data assimilation frameworks such as the CARbon DAta MOdel fraMework (CARDAMOM) and its suite of process‐based models, known as the Data Assimilation Linked Ecosystem Carbon Model (DALEC), are specifically designed for model‐data fusion. This review, motivated by a recent CARDAMOM community workshop, examines the development and applications of CARDAMOM, with an emphasis on its role in advancing ecosystem process understanding. CARDAMOM employs a Bayesian approach, using a Markov Chain Monte Carlo algorithm to enable data‐driven calibration of DALEC parameters and initial states (i.e., carbon pool sizes) through observation operators. CARDAMOM's unique ability to retrieve localized model process parameters from diverse datasets—ranging from in situ measurements to global satellite observations—makes it a highly flexible tool for analyzing spatially variable ecosystem responses to environmental change. However, assimilating these data also presents challenges, including data quality issues that propagate into model skill, as well as trade‐offs between model complexity, parameter equifinality, and predictive performance. We discuss potential solutions to these challenges, such as reducing parameter equifinality by incorporating new observations. This review also offers community recommendations for incorporating emerging datasets, integrating machine learning techniques, strengthening collaboration with remote sensing, field, and modeling communities, and expanding CARDAMOM's relevance for localized ecosystem monitoring and decision‐making. CARDAMOM enables a deep, mechanistic understanding of terrestrial ecosystem dynamics that cannot be achieved through empirical analyses of observational datasets or weakly constrained models alone. 

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 Congo Basin hosts the world's second largest rainforest and is a major rainfall center. However, the primary sources of moisture needed to maintain this forest, either from evapotranspiration (ET) or advection from the ocean, remain unclear. We use satellite observations of the deuterium content of water vapor (), solar induced fluorescence (SIF), precipitation, and atmospheric reanalysis to examine the relative contribution of ET to moisture in the free troposphere. We find that SIF, an indicator of photosynthesis, covaries within early rainy seasons, suggesting that ET is an important contributor to atmospheric moisture in both the spring and fall rainy seasons. However, the relative contribution of ET to the free tropospheric moisture varies between the two rainy seasons. Observedrelative to a range of observationally constrained, isotopic mixing models representative of water vapor coming from land suggests thatof the free tropospheric moisture come from ET in February, andin April, versusin August andin October. Reanalysis indicate that this difference between seasons is due to increased advection of ocean air during the fall season, thus reducing the relative contribution of ET to the Congo Basin in the fall. In addition, ET is the primary atmospheric moisture source in the winter and summer dry seasons, consistent with estimates reported in literature. Our results highlight the importance of ET from the Congo rainforest as an important source of moisture for initiating the rainy seasons. 

Abstract A constellation of satellites is now in orbit providing information about terrestrial carbon and water storage and fluxes. These combined observations show that the tropical biosphere has changed significantly in the last 2 decades from the combined effects of climate variability and land use. Large areas of forest have been cleared in both wet and dry forests, increasing the source of carbon to the atmosphere. Concomitantly, tropical fire emissions have declined, at least until 2016, from changes in land‐use practices and rainfall, increasing the net carbon sink. Measurements of carbon stocks and fluxes from disturbance and recovery and of vegetation photosynthesis show significant regional variability of net biosphere exchange and gross primary productivity across the tropics and are tied to seasonal and interannual changes in water fluxes and storage. Comparison of satellite based estimates of evapotranspiration, photosynthesis, and the deuterium content of water vapor with patterns of total water storage and rainfall demonstrate the presence of vegetation‐atmosphere interactions and feedback mechanisms across tropical forests. However, these observations of stocks, fluxes and inferred interactions between them do not point unambiguously to either positive or negative feedbacks in carbon and water exchanges. These ambiguities highlight the need for assimilation of these new measurements with Earth System models for a consistent assessment of process interactions, along with focused field campaigns that integrate ground, aircraft and satellite measurements, to quantify the controlling carbon and water processes and their feedback mechanisms.