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Recent observations show anomalously high methane growth in 2020, which has been attributed to increased wetland emissions and decreased OH from lower COVID‐19 nitrogen oxide (NOx) emissions. NOx is not the only species that affects OH—isoprene, the most significant non‐methane hydrocarbon by total emissions, is oxidized by OH, which can deplete OH during periods of high emissions. Using satellite isoprene retrievals from the Cross‐track infrared sounder (CrIS), we find anomalously high isoprene columns during 2020, coincident with high methane growth. Isoprene's oxidation produces carbon monoxide, which can be transported over longer distances and decrease OH outside of isoprene source regions. Elevated isoprene concentrations may have contributed 13% (bounds: 10%–28%) of 2020's methane growth if we assume no change in NOx emissions in 2020. Since COVID‐19 decreased anthropogenic NOx emissions, this estimate is an upper‐limit and may depend on whether isoprene or NO emissions drove this isoprene anomaly.

This paper presents an introduction to the Southern Hemisphere High Altitude Experiment on Particle Nucleation and Growth (SALTENA). This field campaign took place between December 2017 and June 2018 (wet to dry season) at Chacaltaya (CHC), a GAW (Global Atmosphere Watch) station located at 5,240 m MSL in the Bolivian Andes. Concurrent measurements were conducted at two additional sites in El Alto (4,000 m MSL) and La Paz (3,600 m MSL). The overall goal of the campaign was to identify the sources, understand the formation mechanisms and transport, and characterize the properties of aerosol at these stations. State-of-the-art instruments were brought to the station complementing the ongoing permanent GAW measurements, to allow a comprehensive description of the chemical species of anthropogenic and biogenic origin impacting the station and contributing to new particle formation. In this overview we first provide an assessment of the complex meteorology, airmass origin, and boundary layer–free troposphere interactions during the campaign using a 6-month high-resolution Weather Research and Forecasting (WRF) simulation coupled with Flexible Particle dispersion model (FLEXPART). We then show some of the research highlights from the campaign, including (i) chemical transformation processes of anthropogenic pollution while the air masses are transported to the CHC station from the metropolitan area of La Paz–El Alto, (ii) volcanic emissions as an important source of atmospheric sulfur compounds in the region, (iii) the characterization of the compounds involved in new particle formation, and (iv) the identification of long-range-transported compounds from the Pacific or the Amazon basin. We conclude the article with a presentation of future research foci. The SALTENA dataset highlights the importance of comprehensive observations in strategic high-altitude locations, especially the undersampled Southern Hemisphere.

This paper presents a regional climate system model RCM–CLM–CN–DV and its validation over Tropical Africa. The model development involves the initial coupling between the ICTP regional climate model RegCM4.3.4 (RCM) and the Community Land Model version 4 (CLM4) including models of carbon–nitrogen dynamics (CN) and vegetation dynamics (DV), and further improvements of the models. Model improvements derive from the new parameterization from CLM4.5 that addresses the well documented overestimation of gross primary production (GPP), a refinement of stress deciduous phenology scheme in CN that addresses a spurious LAI fluctuation for drought-deciduous plants, and the incorporation of a survival rule into the DV model to prevent tropical broadleaf evergreens trees from growing in areas with a prolonged drought season. The impact of the modifications on model results is documented based on numerical experiments using various subcomponents of the model. The performance of the coupled model is then validated against observational data based on three configurations with increasing capacity: RCM–CLM with prescribed leaf area index and fractional coverage of different plant functional types (PFTs); RCM–CLM–CN with prescribed PFTs coverage but prognostic plant phenology; RCM–CLM–CN–DV in which both the plant phenology and PFTs coverage are simulated by the model. Results from these three models are compared against the FLUXNET up-scaled GPP and ET data, LAI and PFT coverages from remote sensing data including MODIS and GIMMS, University of Delaware precipitation and temperature data, and surface radiation data from MVIRI and SRB. Our results indicate that the models perform well in reproducing the physical climate and surface radiative budgets in the domain of interest. However, PFTs coverage is significantly underestimated by the model over arid and semi-arid regions of Tropical Africa, caused by an underestimation of LAI in these regions by the CN model that gets exacerbated through vegetation dynamics in RCM–CLM–CN–DV.

Primarily driven by concern about rising levels of atmospheric CO 2 , ecologists and earth system scientists are collecting vast amounts of data related to the carbon cycle. These measurements are generally time consuming and expensive to make, and, unfortunately, we live in an era where research funding is increasingly hard to come by. Thus, important questions are: \"Which data streams provide the most valuable information?\" and \"How much data do we need?\" These questions are relevant not only for model developers, who need observational data to improve, constrain, and test their models, but also for experimentalists and those designing ecological observation networks. Here we address these questions using a model-data fusion approach. We constrain a process-oriented, forest ecosystem C cycle model with 17 different data streams from the Harvard Forest (Massachusetts, USA). We iteratively rank each data source according to its contribution to reducing model uncertainty. Results show the importance of some measurements commonly unavailable to carbon-cycle modelers, such as estimates of turnover times from different carbon pools. Surprisingly, many data sources are relatively redundant in the presence of others and do not lead to a significant improvement in model performance. A few select data sources lead to the largest reduction in parameter-based model uncertainty. Projections of future carbon cycling were poorly constrained when only hourly net-ecosystem-exchange measurements were used to inform the model. They were well constrained, however, with only 5 of the 17 data streams, even though many individual parameters are not constrained. The approach taken here should stimulate further cooperation between modelers and measurement teams and may be useful in the context of setting research priorities and allocating research funds.

This article is a Commentary on Eller et al., 226: 1622–1637, and Sabot et al., 226: 1638–1655.

This study combines a conceptual modeling approach and a physical process–based modeling approach to diagnose and understand the equilibrium state(s) of coupled biosphere‐atmosphere models using the NCAR CAM3‐CLM3‐DGVM model as an example, with a focus on the West Africa and South America regions. First, a conceptual model is parameterized according to results from offline simulations using CAM3‐CLM3 and CLM3‐DGVM. The resulting conceptual model is then used to predict the potential biosphere‐atmosphere equilibrium state(s) for the coupled CAM3‐CLM3‐DGVM model. Finally, simulations using the coupled CAM3‐CLM3‐DGVM model itself are carried out to verify the results of the conceptual model. Only one equilibrium state is found in both the conceptual model and the physically based numerical model. The CLM3 model features excessively high soil evaporation and very low plant transpiration, which leads to high bare‐ground ET and low sensitivity of ET to vegetation cover changes in the land model. Despite the high sensitivity of precipitation to evapotranspiration changes in the atmospheric model, the land model deficiency leads to a high amount of precipitation in the absence of vegetation cover and a low sensitivity of precipitation to vegetation changes in CAM3‐CLM3, two conditions that are found to anchor a coupled biosphere‐atmosphere model to a single equilibrium state. This statement also holds for the up‐to‐date version of the models using CLM4. This study provides an example for the importance of land surface processes in determining the potential equilibrium states of fully coupled biosphere‐atmosphere models. Key Points Conceptual model proves useful to interpret coupled biosphere‐atm model results Land model deficiency anchors the coupled model to a single equilibrium state Land surface processes limit the model sensitivity to vegetation changes

Increasing vapor pressure deficit (VPD) increases atmospheric demand for water. While increased evapotranspiration (ET) in response to increased atmospheric demand seems intuitive, plants are capable of reducing ET in response to increased VPD by closing their stomata. We examine which effect dominates the response to increasing VPD: atmospheric demand and increases in ET or plant response (stomata closure) and decreases in ET. We use Penman‐Monteith, combined with semiempirical optimal stomatal regulation theory and underlying water use efficiency, to develop a theoretical framework for assessing ET response to VPD. The theory suggests that depending on the environment and plant characteristics, ET response to increasing VPD can vary from strongly decreasing to increasing, highlighting the diversity of plant water regulation strategies. The ET response varies due to (1) climate, with tropical and temperate climates more likely to exhibit a positive ET response to increasing VPD than boreal and arctic climates; (2) photosynthesis strategy, with C3 plants more likely to exhibit a positive ET response than C4 plants; and (3) plant type, with crops more likely to exhibit a positive ET response, and shrubs and gymniosperm trees more likely to exhibit a negative ET response. These results, derived from previous literature connecting plant parameters to plant and climate characteristics, highlight the utility of our simplified framework for understanding complex land‐atmosphere systems in terms of idealized scenarios in which ET responds to VPD only. This response is otherwise challenging to assess in an environment where many processes coevolve together. Plain Language Summary Plants can sense increasing dryness in the air and close up the pores on their leaves, preventing water loss. However, drier air also naturally demands more water from the land surface. Here we develop a simplified theory for when land surface water loss increases (atmospheric demand dominates) or decreases (plant response dominates) in response to increased dryness in the air. This theory provides intuition for how ecosystems regulate water in response to changes in atmospheric dryness. According to the theory, ecosystems are capable of broad range of behavior in response to increased atmospheric dryness, from strongly reducing water loss to allowing large increases in water loss. Ecosystem behavior depends both on environmental conditions and plant type. Key Points We derive a simplified analytical model for ecosystem‐scale evapotranspiration response to changes in vapor pressure deficit Ecosystems exhibit a range of behavior, from reductions to increases in evapotransipration, in response to increasing vapor pressure deficit The choice of stomatal conductance model fundamentally alters the relationship between evapotranspiration and vapor pressure deficit

Emissions of biogenic volatile organic compounds (BVOCs) are a crucial component of biosphere–atmosphere interactions. In northern latitudes, climate change is amplified by feedback processes in which BVOCs have a recognized, yet poorly quantified role, mainly due to a lack of measurements and concomitant modeling gaps. Hence, current Earth system models mostly rely on temperature responses measured on vegetation from lower latitudes, rendering their predictions highly uncertain. Here, we show how tundra isoprene emissions respond vigorously to temperature increases, compared to model results. Our unique dataset of direct eddy covariance ecosystem-level isoprene measurements in two contrasting ecosystems exhibited Q 10 (the factor by which the emission rate increases with a 10 °C rise in temperature) temperature coefficients of up to 20.8, that is, 3.5 times the Q 10 of 5.9 derived from the equivalent model calculations. Crude estimates using the observed temperature responses indicate that tundra vegetation could enhance their isoprene emissions by up to 41% (87%)—that is, 46% (55%) more than estimated by models—with a 2 °C (4°C) warming. Our results demonstrate that tundra vegetation possesses the potential to substantially boost its isoprene emissions in response to future rising temperatures, at rates that exceed the current Earth system model predictions.

Better understanding of factors that control the global carbon cycle could increase confidence in climate projections. Previous studies found good correlation between the growth rate of atmospheric CO₂ concentration and El Niño–Southern Oscillation (ENSO). The growth rate of atmospheric CO₂ increases during El Niño but decreases during La Niña. In this study, long-term simulations of the Earth system models (ESMs) in phase 5 of the Coupled Model Intercomparison Project archive were used to examine the interannual carbon flux variability associated with ENSO. The ESMs simulate the relationship reasonably well with a delay of several months between ENSO and the changes in atmospheric CO₂. The increase in atmospheric CO₂ associated with El Niño is mostly caused by decreasing net primary production (NPP) in the ESMs. It is suggested that NPP anomalies over South Asia are at their maxima during boreal spring; therefore, the increase in CO₂ concentration lags 4–5 months behind the peak phase of El Niño. The decrease in NPP during El Niño may be caused by decreased precipitation and increased temperature over tropical regions. Furthermore, systematic errors may exist in the ESM-simulated temperature responses to ENSO phases over tropical land areas, and these errors may lead to an overestimation of ENSO-related NPP anomalies. In contrast, carbon fluxes from heterotrophic respiration and natural fires are likely underestimated in the ESMs compared with offline model results and observational estimates, respectively. These uncertainties should be considered in long-term projections that include climate–carbon feedbacks.