The terrestrial biosphere can release or absorb the greenhouse gases, carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O), and therefore has an important role in regulating atmospheric composition and climate. Anthropogenic activities such as land-use change, agriculture and waste management have altered terrestrial biogenic greenhouse gas fluxes, and the resulting increases in methane and nitrous oxide emissions in particular can contribute to climate change. The terrestrial biogenic fluxes of individual greenhouse gases have been studied extensively, but the net biogenic greenhouse gas balance resulting from anthropogenic activities and its effect on the climate system remains uncertain. Here we use bottom-up (inventory, statistical extrapolation of local flux measurements, and process-based modelling) and top-down (atmospheric inversions) approaches to quantify the global net biogenic greenhouse gas balance between 1981 and 2010 resulting from anthropogenic activities and its effect on the climate system. We find that the cumulative warming capacity of concurrent biogenic methane and nitrous oxide emissions is a factor of about two larger than the cooling effect resulting from the global land carbon dioxide uptake from 2001 to 2010. This results in a net positive cumulative impact of the three greenhouse gases on the planetary energy budget, with a best estimate (in petagrams of CO₂ equivalent per year) of 3.9 ± 3.8 (top down) and 5.4 ± 4.8 (bottom up) based on the GWP100 metric (global warming potential on a 100-year time horizon). Our findings suggest that a reduction in agricultural methane and nitrous oxide emissions, particularly in Southern Asia, may help mitigate climate change.

© 2016 Macmillan Publishers Limited.

Observed intensification of precipitation extremes, responsible for extensive societal impacts, are widely attributed to anthropogenic sources, which may include indirect effects of agricultural irrigation. However quantifying the effects of irrigation on far-downstream climate remains a challenge. We use three paired Community Earth System Model simulations to assess mechanisms of irrigation-induced precipitation trends and extremes in the conterminous US and the effect on the terrestrial carbon sink. Results suggest precipitation enhancement in the central US reduced drought conditions and increased regional carbon uptake, while further downstream, the heaviest precipitation events were more frequent and intense. Specifically, moisture advection from irrigation in the western U.S. and recycling of enhanced local convective precipitation produced very-heavy storm events that were 11% more intense and occurred 23% more frequently in the densely populated greater New York City region.

The NASA Carbon Monitoring System (CMS) Flux Project aims to attribute changes in the atmospheric accumulation of carbon dioxide to spatially resolved fluxes by utilizing the full suite of NASA data, models, and assimilation capabilities. For the oceanic part of this project, we introduce ECCO2-Darwin, a new ocean biogeochemistry general circulation model based on combining the following pre-existing components: (i) a full-depth, eddying, global-ocean configuration of the Massachusetts Institute of Technology general circulation model (MITgcm), (ii) an adjoint-method-based estimate of ocean circulation from the Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) project, (iii) the MIT ecosystem model “Darwin”, and (iv) a marine carbon chemistry model. Air–sea gas exchange coefficients and initial conditions of dissolved inorganic carbon, alkalinity, and oxygen are adjusted using a Green’s Functions approach in order to optimize modeled air–sea CO₂ fluxes. Data constraints include observations of carbon dioxide partial pressure (pCO₂) for 2009–2010, global air–sea CO₂ flux estimates, and the seasonal cycle of the Takahashi et al. (2009) Atlas. The model sensitivity experiments (or Green’s Functions) include simulations that start from different initial conditions as well as experiments that perturb air–sea gas exchange parameters and the ratio of particulate inorganic to organic carbon. The Green’s Functions approach yields a linear combination of these sensitivity experiments that minimizes model-data differences. The resulting initial conditions and gas exchange coefficients are then used to integrate the ECCO2-Darwin model forward. Despite the small number (six) of control parameters, the adjusted simulation is significantly closer to the data constraints (37% cost function reduction, i.e., reduction in the model-data difference, relative to the baseline simulation) and to independent observations (e.g., alkalinity). The adjusted air–sea gas exchange parameter differs by only 3% from the baseline value and has little impact (−0.1%) on the cost function. The particulate inorganic to organic carbon ratio was increased more than threefold and reduced the cost function by 22% relative to the baseline integration, indicating a significant influence of biology on air–sea gas exchange. The largest contribution to cost reduction (35%) comes from the adjustment of initial conditions. In addition to reducing biases relative to observations, the adjusted simulation exhibits smaller model drift than the baseline. We estimate drift by integrating the model with repeated 2009 atmospheric forcing for seven years and find a volume-weighted drift reduction of, for example, 12.5% for nitrate and 30% for oxygen in the top 300 m. Although there remain several regions with large model-data discrepancies, for example, overly strong carbon uptake in the Southern Ocean, the adjusted simulation is a first step towards a more accurate representation of the ocean carbon cycle at high spatial and temporal resolution.

Gridded precipitation-gauge observations and global atmospheric reanalysis are combined to develop an analogue method for detecting the occurrence of heavy precipitation events based on the prevailing large-scale atmospheric conditions. Combinations of different atmospheric variables for circulation features (geopotential height and wind vector) and moisture plumes (surface specific humidity, column precipitable water, and precipitable water up to 500hPa) are examined to construct the analogue schemes for the winter (DJF) of the Pacific Coast California (PCCA) and the summer (JJA) of the Midwestern United States (MWST). The detection diagnostics of various analogue schemes are calibrated with 27-yr (1979–2005) and then validated with 9-yr (2006–2014) NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). All of the analogue schemes are found to significantly improve upon MERRA precipitation in characterizing the number and interannual variations of observed heavy precipitation events in the MWST which is one of weakest regions for MERRA summer precipitation. When evaluated with the late 20th century simulations from an ensemble of climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), all analogue schemes produce model medians of heavy precipitation frequency that are more consistent with observations and have smaller inter-model discrepancies when compared with the model-based precipitation. Further, the performances of analogue schemes with vector winds are comparable to those of geopotential height, and no analogue scheme with one of three water vapor content variables is clearly superior to another. Under two radiative forcing scenarios (Representative Concentration Pathways 4.5 and 8.5), the CMIP5-based analogue schemes produce a trend in the occurrence of heavy events through the 21st century consistent with the model-based precipitation, but with smaller inter-model disparity. The strongest reduction in the disparity of the results is seen for the RCP8.5 scenario. The median trends in DJF heavy precipitation frequency for PCCA are positive, but for JJA heavy event frequency over the MWST region, the median trends are slightly negative. Overall, the presented analyses highlight the potential of the analogue as a powerful diagnostic tool for model deficiencies and its complementarity to an evaluation that considers modeled precipitation alone to assess heavy precipitation frequency. The consistency found here between projections from analogues and model precipitation increases confidence in projected heavy precipitation frequency changes in a warming climate.