Carbon dioxide and climate impulse response functions for the computation of greenhouse gas metrics: A multi-model analysis

The responses of carbon dioxide (CO2) and other climate variables to an emission pulse of CO2 into the atmosphere are often used to compute the Global Warming Potential (GWP) and Global Temperature change Potential (GTP), to characterize the response timescales of Earth System models, and to build reduced-form models. In this carbon cycle-climate model intercomparison project, which spans the full model hierarchy, we quantify responses to emission pulses of different magnitudes injected under different conditions. The CO2 response shows the known rapid decline in the first few decades followed by a millennium-scale tail. For a 100 Gt-C emission pulse added to a constant CO2 concentration of 389 ppm, 25 ± 9% is still found in the atmosphere after 1000 yr; the ocean has absorbed 59 ± 12% and the land the remainder (16 ± 14%). The response in global mean surface air temperature is an increase by 0.20 ± 0.12 °C within the first twenty years; thereafter and until year 1000, temperature decreases only slightly, whereas ocean heat content and sea level continue to rise. Our best estimate for the Absolute Global Warming Potential, given by the time-integrated response in CO2 at year 100 multiplied by its radiative efficiency, is 92.5 × 10−15 yr W m−2 per kg-CO2. This value very likely (5 to 95% confidence) lies within the range of (68 to 117) × 10−15 yr W m−2 per kg-CO2. Estimates for time-integrated response in CO2 published in the IPCC First, Second, and Fourth Assessment and our multi-model best estimate all agree within 15% during the first 100 yr. The integrated CO2 response, normalized by the pulse size, is lower for pre-industrial conditions, compared to present day, and lower for smaller pulses than larger pulses. In contrast, the response in temperature, sea level and ocean heat content is less sensitive to these choices. Although, choices in pulse size, background concentration, and model lead to uncertainties, the most important and subjective choice to determine AGWP of CO2 and GWP is the time horizon

A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided

Detecting an external influence on recent changes in oceanic oxygen using an optimal fingerprinting method

Ocean deoxygenation has been observed in all major ocean basins over the past 50 yr. Although this signal is largely consistent with oxygen changes expected from anthropogenic climate change, the contribution of external forcing to recent deoxygenation trends relative to natural internal variability is yet to be established. Here we conduct a formal optimal fingerprinting analysis to investigate if external forcing has had a detectable influence on observed dissolved oxygen concentration ([O₂]) changes between ∼1970 and ∼1992 using simulations from two Earth System Models (MPI-ESM-LR and HadGEM2-ES). We detect a response to external forcing at a 90% confidence level and find that observed [O₂] changes are inconsistent with internal variability as simulated by models. This result is robust in the global ocean for depth-averaged (1-D) zonal mean patterns of [O₂] change in both models. Further analysis with the MPI-ESM-LR model shows similar positive detection results for depth-resolved (2-D) zonal mean [O₂] changes globally and for the Pacific Ocean individually. Observed oxygen changes in the Atlantic Ocean are indistinguishable from natural internal variability. Simulations from both models consistently underestimate the amplitude of historical [O₂] changes in response to external forcing, suggesting that model projections for future ocean deoxygenation may also be underestimated

Creating a framework for conducting randomized clinical trials during disease outbreaks

As in the ongoing Covid-19 pandemic, global outbreaks of infectious illnesses often develop quickly and resolve in unpredictable ways. In this report, strategies to develop high-quality evidence to guide the development of new therapies are proposed

The HadGEM2-ES implementation of CMIP5 centennial simulations

The scientific understanding of the Earth's climate system, including thecentral question of how the climate system is likely to respond tohuman-induced perturbations, is comprehensively captured in GCMs and EarthSystem Models (ESM). Diagnosing the simulated climate response, andcomparing responses across different models, is crucially dependent ontransparent assumptions of how the GCM/ESM has been driven - especiallybecause the implementation can involve subjective decisions and may differbetween modelling groups performing the same experiment. This paper outlinesthe climate forcings and setup of the Met Office Hadley Centre ESM, HadGEM2-ES for the CMIP5 set of centennial experiments. We document theprescribed greenhouse gas concentrations, aerosol precursors, stratosphericand tropospheric ozone assumptions, as well as implementation of land-usechange and natural forcings for the HadGEM2-ES historical and futureexperiments following the Representative Concentration Pathways. Inaddition, we provide details of how HadGEM2-ES ensemble members wereinitialised from the control run and how the palaeoclimate and AMIPexperiments, as well as the "emission-driven" RCP experiments wereperformed

Th17 promotes acute rejection following liver transplantation in rats*

T help cell 17 (Th17), recently identified as a new subset of CD4+ T cells, has been implicated in autoimmune diseases, tumor immunity, and transplant rejection. To investigate the role of Th17 in acute hepatic rejection, a rat model of allogeneic liver transplantation (Dark Agouti (DA) to Brown Norway (BN)) was established and isogeneic liver transplantation (BN to BN) was used as controls in the study. The expression of Th17-related cytokines in the liver and peripheral blood was determined by immunohistochemistry, flow cytometry, enzyme-linked immunosorbent assay (ELISA), or real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR). Strong expression of interleukin-17A (IL-17A), IL-6, transforming growth factor-β (TGF-β), IL-8, and myeloperoxidase (MPO) was observed in liver allografts. The ratios of Th17 to CD4+ lymphocytes in the liver and peripheral blood were dramatically increased in the allograft group compared with the control (P<0.01). Secreted IL-17 and IL-6 in liver homogenate and serum were significantly elevated in the allograft group, while secreted TGF-β was increased in liver homogenate and decreased in serum compared with the control (P<0.01). The messenger RNA (mRNA) levels of IL-17, IL-21, and IL-23 were enhanced in the allografts compared with the control (P<0.01). Correlation analysis showed significant correlations between IL-17 and IL-6 and TGF-β and between IL-17 and IL-21 and IL-23. The present study demonstrates that Th17 plays a role in promoting rat liver allograft rejection