Joint Global Change Research Institute

About: Joint Global Change Research Institute is a facility organization based out in Riverdale Park, Maryland, United States. It is known for research contribution in the topics: Greenhouse gas & Climate change. The organization has 197 authors who have published 934 publications receiving 62390 citations.

The Emissions Model Intercomparison Project (Emissions-MIP): quantifying model sensitivity to emission characteristics

Abstract: Abstract. Anthropogenic emissions of aerosols and precursor compounds are known to significantly affect the energy balance of the Earth-atmosphere system, alter the formation of clouds and precipitation, and have substantial impact on human health and the environment. Global models are an essential tool for examining the impacts of these emissions. In this study, we examine the sensitivity of model results to the assumed height of SO2 injection, seasonality of SO2 and BC emissions, and the assumed fraction of SO2 emissions that is injected into the atmosphere as SO4 in 11 climate and chemistry models, including both chemical transport models and the atmospheric component of Earth system models. We find a large variation in atmospheric lifetime across models for SO2, SO4, and BC, with a particularly large relative variation for SO2, which indicates that fundamental aspects of atmospheric sulfur chemistry remain uncertain. Of the perturbations examined in this study, the assumed height of SO2 injection had the largest overall impacts, particularly on global mean net radiative flux (maximum difference of -0.35 W m-2), SO2 lifetime over northern hemisphere land (maximum difference of 0.8 days), surface SO2 concentration (up to 59 % decrease), and surface sulfate concentration (up to 23 % increase). Emitting SO2 at height consistently increased SO2 and SO4 column burdens and shortwave cooling, with varying magnitudes, but had inconsistent effects across models on the sign of the change in implied cloud forcing. The assumed SO4 emission fraction also had a significant impact on net radiative flux and surface sulfate concentration. Because these properties are not standardized across models this is a source of inter-model diversity typically neglected in model intercomparisons. These results imply a need to assure that anthropogenic emission injection height and SO4 emission fraction are accurately and consistently represented in global models.

Reply on RC2

Abstract: Abstract. Continental biogeochemical models are commonly used to predict the effect of land use, exogenous organic matter input or climate change on soil greenhouse gas emission. However, they cannot be used for this purpose to investigate the effect of soil contamination, while contamination affects several soil processes and concerns a large fraction of land surface. For that, in this study we implemented a commonly used model estimating soil nitrogen (N) emissions, the DeNitrification DeCompostion (DNDC) model, with a function taking into account soil copper (Cu) contamination in nitrate production control. Then, we aimed at using this model to predict N₂O-N, NO₂-N, NO-N and NH₄-N emissions in the presence of contamination and in the context of changes in precipitations. Initial incubations of soils were performed at different soil moisture levels in order to mimic expected rainfall patterns during the next decades and in particular drought and excess of water. Then, a bioassay was used in the absence or presence of Cu to assess the effect of the single (moisture) or double stress (moisture and Cu) on soil nitrate production. Data of nitrate production obtained through a gradient of Cu under each initial moisture incubation were used to parameterise the DNDC model and to estimate soil N emission considering the various effects of Cu. Whatever the initial moisture incubation, experimental results showed a NO₃-N decreasing production when Cu was added but depending on soil moisture. The DNDC-Cu version we proposed was able to reproduce these observed Cu effects on soil nitrate concentration with r²â€‰> 0.99 and RMSE < 10 % for all treatments in the DNDC-Cu calibration range (> 40 % of the water holding capacity) but showed poor performances for the dry treatments. We modelled a Cu effect inducing an increase in NH₄-N soil concentration and emissions due to a reduced nitrification activity and therefore a decrease in NO₃-N, N₂O-N and NO_x-N concentrations and emissions. The effect of added Cu predicted by the model was larger on N₂-N and N₂O-N emissions than on the other N species and larger for the soils incubated under constant than variable moisture. Our work shows that soil contamination can be considered in continental biogeochemical models to better predict soil greenhouse gas emissions.

New features, broader accessibility, and improved performance of the Hector v3 simple carbon/climate model

Abstract: &lt;p&gt;Hector is a carbon/climate model capable of emulating Earth System Model outputs at the global scale and is able to reproduce historical observations well. Like other participating models of the Reduced Complexity Model Intercomparison Project, Hector is a computationally efficient source of climate projections and thus has a wide range of applications such as scenario generation, coupling with integrated assessment models, outreach, education, and policy making. Hector version 3 includes a number of new features: carbon tracking, permafrost, improved land-ocean warming contrast, and a web browser-accessible interface. Here we summarize these developments and discuss how they improve the model&amp;#8217;s performance and broaden its potential user base.&amp;#160;&lt;/p&gt;

Catastrophic thresholds, bayesian learning and the robustness of climate policy recommendations

Abstract: How does risk and uncertainty in climate thresholds impact optimal short-run mitigation? This paper contrasts the near-term mitigation consequences of using an expected value, stochastic programming (SP), and stochastic control model to capture the policy effects of uncertain climate thresholds. The risk of threshold outcomes increases the expected damage. The passive learning associated with SP creates an extra incentive to mitigate and promptly to reduce the damage from the remaining threshold hazards. The active learning associated with a stochastic control model creates yet another incentive to do near-term mitigation in order to postpone reaching harmful thresholds.