Boraneamines tend to have close N-H δ+ ‚‚‚ δ-H-B contacts as a result of the intermolecular interaction of the NH proton with the BH bond by a novel type of hydrogen bond (the dihydrogen bond). A CSD structural search provides characteristic metric data for the interaction: the H‚‚‚H distance is in the range 1.7-2.2 Å, and the N-H‚‚‚H group tends to be linear while B-H‚‚‚H tends to be bent. The reported X-ray structure of BH 3 NH 3 seemed to provide a singular exception in having bent N-H‚‚‚H and linear B-H‚‚‚H. Our neutron diffraction structure of BH 3 NH 3 now shows that the B and N atoms must be reversed from the assignment previously published. With the correct assignment we find the expected bent B-H‚‚‚H and linear N-H‚‚‚H arrangement in the closest intermolecular N-H‚‚‚H-B interaction (d HH ) 2.02 Å).

B y the time the World Health Organization declared COVID-19 (scientifically referred to as the severe acute respiratory syndrome-coronavirus 2 or SARS-CoV-2) a pandemic on 11 March 2020, the virus had already spread from China to other Asian countries, Europe and the United States. As of 5 July 2020, cases have been identified in 188 countries or regions 1. This has led to unprecedented enforced and voluntary restrictions on travel and work. This in turn has led to reductions of both GHG emissions and air pollutants 2-4. Analysis of mobility data from Google 5 and Apple 6 shows that mobility declined by 10% or more during April 2020 in all but one of the 125 nations tracked. Mobility declined by 80% in five or more nations (Supplementary Fig. 1). Associated declines in air pollution have been observed from satellite data and from local ground-based observations 7,8. The large pollution declines are expected to be temporary as pollution levels are already returning to near-normal in parts of Asia 9,10. Here we build an estimate of emission changes in GHGs and air pollution due to the COVID-19 global restrictions during the period February-June 2020 and project these into the future. These emission changes are then used to make a prediction of the resultant global temperature response. We examine the temperature response of a direct recovery to pre-COVID-19 national policies and emission levels, and also explore responses where the economic recovery to COVID-19 is driven by either a green stimulus package or an increase in fossil fuel use. Emission trends Bottom-up emission-trend analyses have traditionally relied on laborious collection of various energy-industry-related indicators and statistics from multiple sources 11. The unprecedented recent access to global mobility data from Google and Apple gives a unique opportunity to compare trends across many countries with a consistent approach. We use these data to develop a new method of emission-trend analysis. The advantage over previous approaches is the possibility of near-real-time analysis, national granularity and a systematic consistent approach across nations and over time. The disadvantages are the loss of a direct connection between energy and emissions and the need to make assumptions about these relationships. There are also disadvantages over the short time history of the mobility data and opacity from the data providers around their detailed methodologies and uncertainties. Here we make a simple set of assumptions to deduce estimates of emissions change from the mobility data and test the estimates extensively against the approach of Le Quéré et al. 3. Google and Apple mobility changes and the Le Quéré et al. 3 data all indicate that >50% of the world's population reduced travel by >50% during April 2020 (Fig. 1a). Google mobility trends indicate that >80% of the population in the 114 countries in the dataset (4 billion people) reduced their travel by >50%. Google mobility data and emission reduction estimates based on confinement level analysis in L...

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The usefulness of previous Coupled Model Intercomparison Project (CMIP) exercises has been hampered by a lack of radiative forcing information. This has made it difficult to understand reasons for differences between model responses. Effective radiative forcing (ERF) is easier to diagnose than traditional radiative forcing in global climate models (GCMs) and is more representative of the eventual temperature response. Here we examine the different methods of computing ERF in two GCMs. We find that ERF computed from a fixed sea surface temperature (SST) method (ERF_fSST) has much more certainty than regression based methods. Thirty year integrations are sufficient to reduce the 5–95% confidence interval in global ERF_fSST to 0.1 W m−2. For 2xCO2 ERF, 30 year integrations are needed to ensure that the signal is larger than the local confidence interval over more than 90% of the globe. Within the ERF_fSST method there are various options for prescribing SSTs and sea ice. We explore these and find that ERF is only weakly dependent on the methodological choices. Prescribing the monthly averaged seasonally varying model's preindustrial climatology is recommended for its smaller random error and easier implementation. As part of CMIP6, the Radiative Forcing Model Intercomparison Project (RFMIP) asks models to conduct 30 year ERF_fSST experiments using the model's own preindustrial climatology of SST and sea ice. The Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) will also mainly use this approach. We propose this as a standard method for diagnosing ERF and recommend that it be used across the climate modeling community to aid future comparisons.

Precipitation is expected to respond differently to various drivers of anthropogenic climate change. We present the first results from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), where nine global climate models have perturbed CO2, CH4, black carbon, sulfate, and solar insolation. We divide the resulting changes to global mean and regional precipitation into fast responses that scale with changes in atmospheric absorption and slow responses scaling with surface temperature change. While the overall features are broadly similar between models, we find significant regional intermodel variability, especially over land. Black carbon stands out as a component that may cause significant model diversity in predicted precipitation change. Processes linked to atmospheric absorption are less consistently modeled than those linked to top‐of‐atmosphere radiative forcing. We identify a number of land regions where the model ensemble consistently predicts that fast precipitation responses to climate perturbations dominate over the slow, temperature‐driven responses.

Rapid adjustments are responses to forcing agents that cause a perturbation to the top of atmosphere energy budget but are uncoupled to changes in surface warming. Different mechanisms are responsible for these adjustments for a variety of climate drivers. These remain to be quantified in detail. It is shown that rapid adjustments reduce the effective radiative forcing (ERF) of black carbon by half of the instantaneous forcing, but for CO2 forcing, rapid adjustments increase ERF. Competing tropospheric adjustments for CO2 forcing are individually significant but sum to zero, such that the ERF equals the stratospherically adjusted radiative forcing, but this is not true for other forcing agents. Additional experiments of increase in the solar constant and increase in CH4 are used to show that a key factor of the rapid adjustment for an individual climate driver is changes in temperature in the upper troposphere and lower stratosphere.

We investigate the climate response to increased concentrations of black carbon (BC), as part of the Precipitation Driver Response Model Intercomparison Project (PDRMIP). A tenfold increase in BC is simulated by nine global coupled‐climate models, producing a model median effective radiative forcing of 0.82 (ranging from 0.41 to 2.91) W m−2, and a warming of 0.67 (0.16 to 1.66) K globally and 1.24 (0.26 to 4.31) K in the Arctic. A strong positive instantaneous radiative forcing (median of 2.10 W m−2 based on five of the models) is countered by negative rapid adjustments (−0.64 W m−2 for the same five models), which dampen the total surface temperature signal. Unlike other drivers of climate change, the response of temperature and cloud profiles to the BC forcing is dominated by rapid adjustments. Low‐level cloud amounts increase for all models, while higher‐level clouds are diminished. The rapid temperature response is particularly strong above 400 hPa, where increased atmospheric stabilization and reduced cloud cover contrast the response pattern of the other drivers. In conclusion, we find that this substantial increase in BC concentrations does have considerable impacts on important aspects of the climate system. However, some of these effects tend to offset one another, leaving a relatively small median global warming of 0.47 K per W m−2—about 20% lower than the response to a doubling of CO2. Translating the tenfold increase in BC to the present‐day impact of anthropogenic BC (given the emissions used in this work) would leave a warming of merely 0.07 K.

As the global temperature increases with changing climate, precipitation rates and patterns are affected through a wide range of physical mechanisms. The globally averaged intensity of extreme precipitation also changes more rapidly than the globally averaged precipitation rate. While some aspects of the regional variation in precipitation predicted by climate models appear robust, there is still a large degree of intermodel differences unaccounted for. Individual drivers of climate change initially alter the energy budget of the atmosphere, leading to distinct rapid adjustments involving changes in precipitation. Differences in how these rapid adjustment processes manifest themselves within models are likely to explain a large fraction of the present model spread and better quantifications are needed to improve precipitation predictions. Here, the authors introduce the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), where a set of idealized experiments designed to understand the role of different climate forcing mechanisms were performed by a large set of climate models. PDRMIP focuses on understanding how precipitation changes relating to rapid adjustments and slower responses to climate forcings are represented across models. Initial results show that rapid adjustments account for large regional differences in hydrological sensitivity across multiple drivers. The PDRMIP results are expected to dramatically improve understanding of the causes of the present diversity in future climate projections.