Increased Amazon carbon emissions mainly from decline in law enforcement.

The Amazon forest carbon sink is declining, mainly as a result of land-use and climate change1-4. Here we investigate how changes in law enforcement of environmental protection policies may have affected the Amazonian carbon balance between 2010 and 2018 compared with 2019 and 2020, based on atmospheric CO2 vertical profiles5,6, deforestation7 and fire data8, as well as infraction notices related to illegal deforestation9. We estimate that Amazonia carbon emissions increased from a mean of 0.24 ± 0.08 PgC year-1 in 2010-2018 to 0.44 ± 0.10 PgC year-1 in 2019 and 0.52 ± 0.10 PgC year-1 in 2020 (± uncertainty). The observed increases in deforestation were 82% and 77% (94% accuracy) and burned area were 14% and 42% in 2019 and 2020 compared with the 2010-2018 mean, respectively. We find that the numbers of notifications of infractions against flora decreased by 30% and 54% and fines paid by 74% and 89% in 2019 and 2020, respectively. Carbon losses during 2019-2020 were comparable with those of the record warm El Niño (2015-2016) without an extreme drought event. Statistical tests show that the observed differences between the 2010-2018 mean and 2019-2020 are unlikely to have arisen by chance. The changes in the carbon budget of Amazonia during 2019-2020 were mainly because of western Amazonia becoming a carbon source. Our results indicate that a decline in law enforcement led to increases in deforestation, biomass burning and forest degradation, which increased carbon emissions and enhanced drying and warming of the Amazon forests.

Reducing Wet Ammonium Deposition in Rocky Mountain National Park: the Development and Evaluation of A Pilot Early Warning System for Agricultural Operations in Eastern Colorado.

Agricultural emissions are the primary source of ammonia (NH3) deposition in Rocky Mountain National Park (RMNP), a Class I area, that is granted special air quality protections under the Clean Air Act. Between 2014 and 2016, the pilot phase of the Colorado agricultural nitrogen early warning system (CANEWS) was developed for agricultural producers to voluntarily and temporarily minimize emissions of NH3 during periods of upslope winds. The CANEWS was created using trajectory analyses driven by outputs from an ensemble of numerical weather forecasts together with the climatological expertize of human forecasters. Here, we discuss the methods for the CANEWS and offer preliminary analyses of 33 months of the CANEWS based on atmospheric deposition data from two sites in RMNP as well as responses from agricultural producers after warnings were issued. Results showed that the CANEWS accurately predicted 6 of 9 high N deposition weeks at a lower-elevation observation site, but only 4 of 11 high N deposition weeks at a higher-elevation site. Sixty agricultural producers from 39 of Colorado's agricultural operations volunteered for the CANEWS, and a two-way line of communication between agricultural producers and scientists was formed. For each warning issued, an average of 23 producers responded to a postwarning survey. Over 75% of responding CANEWS participants altered their practices after an alert. While the current effort was insufficient to reduce atmospheric deposition, we were encouraged by the collaborative spirit between agricultural, scientific, and resource management communities. Solving a broad and complex social-ecological problem requires both a technological approach, such as the CANEWS, and collaboration and trust from all participants, including agricultural producers, land managers, university researchers, and environmental agencies.

Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade-off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade-off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

Surface-Atmosphere Coupling Scale, the Fate of Water, and Ecophysiological Function in a Brazilian Forest.

Tropical South America plays a central role in global climate. Bowen ratio teleconnects to circulation and precipitation processes far afield, and the global CO2 growth rate is strongly influenced by carbon cycle processes in South America. However, quantification of basin-wide seasonality of flux partitioning between latent and sensible heat, the response to anomalies around climatic norms, and understanding of the processes and mechanisms that control the carbon cycle remains elusive. Here, we investigate simulated surface-atmosphere interaction at a single site in Brazil, using models with different representations of precipitation and cloud processes, as well as differences in scale of coupling between the surface and atmosphere. We find that the model with parameterized clouds/precipitation has a tendency toward unrealistic perpetual light precipitation, while models with explicit treatment of clouds produce more intense and less frequent rain. Models that couple the surface to the atmosphere on the scale of kilometers, as opposed to tens or hundreds of kilometers, produce even more realistic distributions of rainfall. Rainfall intensity has direct consequences for the "fate of water," or the pathway that a hydrometeor follows once it interacts with the surface. We find that the model with explicit treatment of cloud processes, coupled to the surface at small scales, is the most realistic when compared to observations. These results have implications for simulations of global climate, as the use of models with explicit (as opposed to parameterized) cloud representations becomes more widespread.

Evaluating atmospheric CO2 inversions at multiple scales over a highly inventoried agricultural landscape.

An intensive regional research campaign was conducted by the North American Carbon Program (NACP) in 2007 to study the carbon cycle of the highly productive agricultural regions of the Midwestern United States. Forty-five different associated projects were conducted across five US agencies over the course of nearly a decade involving hundreds of researchers. One of the primary objectives of the intensive campaign was to investigate the ability of atmospheric inversion techniques to use highly calibrated CO2 mixing ratio data to estimate CO2 flux over the major croplands of the United States by comparing the results to an inventory of CO2 fluxes. Statistics from densely monitored crop production, consisting primarily of corn and soybeans, provided the backbone of a well studied bottom-up inventory flux estimate that was used to evaluate the atmospheric inversion results. Estimates were compared to the inventory from three different inversion systems, representing spatial scales varying from high resolution mesoscale (PSU), to continental (CSU) and global (CarbonTracker), coupled to different transport models and optimization techniques. The inversion-based mean CO2 -C sink estimates were generally slightly larger, 8-20% for PSU, 10-20% for CSU, and 21% for CarbonTracker, but statistically indistinguishable, from the inventory estimate of 135 TgC. While the comparisons show that the MCI region-wide C sink is robust across inversion system and spatial scale, only the continental and mesoscale inversions were able to reproduce the spatial patterns within the region. In general, the results demonstrate that inversions can recover CO2 fluxes at sub-regional scales with a relatively high density of CO2 observations and adequate information on atmospheric transport in the region.

Africa and the global carbon cycle.

The African continent has a large and growing role in the global carbon cycle, with potentially important climate change implications. However, the sparse observation network in and around the African continent means that Africa is one of the weakest links in our understanding of the global carbon cycle. Here, we combine data from regional and global inventories as well as forward and inverse model analyses to appraise what is known about Africa's continental-scale carbon dynamics. With low fossil emissions and productivity that largely compensates respiration, land conversion is Africa's primary net carbon release, much of it through burning of forests. Savanna fire emissions, though large, represent a short-term source that is offset by ensuing regrowth. While current data suggest a near zero decadal-scale carbon balance, interannual climate fluctuations (especially drought) induce sizeable variability in net ecosystem productivity and savanna fire emissions such that Africa is a major source of interannual variability in global atmospheric CO2. Considering the continent's sizeable carbon stocks, their seemingly high vulnerability to anticipated climate and land use change, as well as growing populations and industrialization, Africa's carbon emissions and their interannual variability are likely to undergo substantial increases through the 21st century.

Weak northern and strong tropical land carbon uptake from vertical profiles of atmospheric CO2.

Measurements of midday vertical atmospheric CO2 distributions reveal annual-mean vertical CO2 gradients that are inconsistent with atmospheric models that estimate a large transfer of terrestrial carbon from tropical to northern latitudes. The three models that most closely reproduce the observed annual-mean vertical CO2 gradients estimate weaker northern uptake of -1.5 petagrams of carbon per year (Pg C year(-1)) and weaker tropical emission of +0.1 Pg C year(-1) compared with previous consensus estimates of -2.4 and +1.8 Pg C year(-1), respectively. This suggests that northern terrestrial uptake of industrial CO2 emissions plays a smaller role than previously thought and that, after subtracting land-use emissions, tropical ecosystems may currently be strong sinks for CO2.

Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models.

Information about regional carbon sources and sinks can be derived from variations in observed atmospheric CO2 concentrations via inverse modelling with atmospheric tracer transport models. A consensus has not yet been reached regarding the size and distribution of regional carbon fluxes obtained using this approach, partly owing to the use of several different atmospheric transport models. Here we report estimates of surface-atmosphere CO2 fluxes from an intercomparison of atmospheric CO2 inversion models (the TransCom 3 project), which includes 16 transport models and model variants. We find an uptake of CO2 in the southern extratropical ocean less than that estimated from ocean measurements, a result that is not sensitive to transport models or methodological approaches. We also find a northern land carbon sink that is distributed relatively evenly among the continents of the Northern Hemisphere, but these results show some sensitivity to transport differences among models, especially in how they respond to seasonal terrestrial exchange of CO2. Overall, carbon fluxes integrated over latitudinal zones are strongly constrained by observations in the middle to high latitudes. Further significant constraints to our understanding of regional carbon fluxes will therefore require improvements in transport models and expansion of the CO2 observation network within the tropics.