The global and regional impacts of climate change under Representative Concentration Pathway forcings and Shared Socioeconomic Pathway socioeconomic scenarios

This paper presents an evaluation of the global and regional consequences of climate change for heat extremes, water resources, river and coastal flooding, droughts, agriculture and energy use. It presents change in hazard and resource base under different rates of climate change (Representative Concentration Pathways: RCP), and socio-economic impacts are estimated for each combination of RCP and Shared Socioeconomic Pathway. Uncertainty in the regional pattern of climate change is characterised by CMIP5 climate model projections. The analysis adopts a novel approach using relationships between level of warming and impact to rapidly estimate impacts under any climate forcing. The projections provided here can be used to inform assessments of the implications of climate change. At the global scale all the consequences of climate change considered here are adverse, with large increases under the highest rates of warming. Under the highest forcing the global average annual chance of a major heatwave increases from 5% now to 97% in 2100, the average proportion of time in drought increases from 7% to 27%, and the average chance of the current 50-year flood increases from 2% to 7%. The socio-economic impacts of these climate changes are determined by socio-economic scenario. There is variability in impact across regions, reflecting variability in projected changes in precipitation and temperature. The range in the estimated impacts can be large, due to uncertainty in future emissions and future socio-economic conditions and scientific uncertainty in how climate changes in response to future emissions. For the temperature-based indicators, the largest source of scientific uncertainty is in the estimated magnitude of equilibrium climate sensitivity, but for the indicators determined by precipitation the largest source is in the estimated spatial and seasonal pattern of changes in precipitation. By 2100 the range across socio-economic scenario is often greater than the range across the forcing levels

Performance of Pattern-Scaled Climate Projections under High-End Warming. Part I: Surface Air Temperature over Land

Pattern scaling is widely used to create climate change projections to investigate future impacts. We consider the performance of pattern scaling for emulating the HadGEM2-ES general circulation model (GCM) paying particular attention to “high end” warming scenarios and to different choices of GCM simulations used to diagnose the climate change patterns. We demonstrate that evaluating pattern-scaling projections by comparing them with GCM simulations containing unforced variability gives a significantly less favorable view of the actual performance of pattern scaling. Using a four-member initial-condition ensemble of HadGEM2-ES simulations, we infer that the root-mean-square errors of pattern-scaled monthly temperature changes over land are less than 0.25°C for global warming up to approximately 3.5°C. Some regional errors are larger than this and, for this GCM, there is a tendency for pattern scaling to underestimate warming over land. For warming above 3.5°C, the pattern-scaled projection errors grow but remain small relative to the climate change signal. We investigate whether patterns diagnosed by pooling GCM experiments from several scenarios are suitable for emulating the GCM under a high-end warming scenario. For global warming up to 3.5°C, pattern scaling using this pooled pattern closely emulates GCM simulations. For warming beyond 3.5°C, pattern-scaling performance is notably improved by using patterns diagnosed only from the high-forcing representative concentration pathway 8.5 (RCP8.5) scenario. Assessments of climate change impacts under high-end warming using pattern-scaling projections could be improved by using change patterns diagnosed from pooled scenarios for projections up to 3.5°C above preindustrial levels and patterns diagnosed from only strong forcing simulations for projecting beyond that. Similar findings are obtained for five other GCMs

The influence of remote aerosol forcing from industrialised economies on the future evolution of East and West African rainfall

Past changes in global industrial aerosol emissions have played a significant role in historical shifts in African rainfall and yet assessment of the impact on African rainfall of near term (10-40 year) potential aerosol emission pathways remains largely unexplored. Whilst existing literature links future aerosol declines to a northward shift of Sahel rainfall, existing climate projections rely on RCP scenarios that do not explore the range of air quality drivers. Here we present projections from two emission scenarios that better envelope the range of potential aerosol emissions. More aggressive emission cuts results in northward shifts of the tropical rain-bands whose signal can emerge from expected internal variability on short, 10-20 year, time horizons. We also show for the first time that this northward shift also impacts East Africa, with evidence of delays to both onset and withdrawal of the Short Rains. However, comparisons of rainfall impacts across models suggest that only certain aspects of both the West and East African model responses may be robust, given model uncertainties. This work motivates the need for wider exploration of air quality scenarios in the climate science community to assess the robustness of these projected changes and to provide evidence to underpin climate adaptation in Africa. In particular, revised estimates of emission impacts of legislated measures every 5-10 years would have a value in providing near term climate adaptation information for African stakeholders

Joint Analysis Of Psychiatric Disorders Increases Accuracy Of Risk Prediction For Schizophrenia, Bipolar Disorder, And Major Depressive Disorder

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Assessing uncertainty in estimates of atmospheric temperature changes from MSU and AMSU using a Monte‐Carlo estimation technique

Measurements made by the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit (AMSU) provide a multidecadal record of global atmospheric temperature change, which have been used by several groups to produce long ‐ term temperature records of thick layers of the atmosphere from the lower troposphere to the lower stratosphere. Here we present an internal uncertainty estimate for the Remote Sensing Systems data sets made using a Monte Carlo approach that includes contributions to the total uncertainty from sampling error, premerge adjustments to each individual satellite, and the merging procedure. The results can be used to estimate uncertainties in this product at all space and time scales of interest to any specific application. On small space and time scales sampling effects dominate. On the longer time scales intersatellite merging is important at all levels and the diurnal adjustment is a critical uncertainty for the two layers that have a significant surface component, particularly over land. A comparison of trends for the globe, tropics, and extratropics between the best estimate data set along with these error estimates and homogenized radiosonde estimates and available MSU/AMSU estimates from other groups is undertaken. This shows consistency between our product and those produced by others within the stated uncertainty for many regions and layers. In almost as many cases, however, the interdata set differences of the estimated trends are too large be accounted for by the internal uncertainty estimates derived herein

Assessing climate risk using ensembles: A novel framework for applying and extending open-source climate risk assessment platforms

Climate change adaptation decisions often require the consideration of risk rather than the environmental hazard alone. One approach for quantifying risk is to use a risk assessment framework which combines information about hazard, exposure and vulnerability to estimate risk in a spatially consistent way. In recent years, publicly available, open-source risk assessment frameworks have been made available, including the CLIMADA platform. Such tools are increasingly being used in combination with ensembles of climate model projections to quantify risk on climate time-scales, presenting the ensemble spread as a measure of climate model uncertainty. As climate models are computationally expensive to run, this quantification of uncertainty derived from the ensemble of projections is often limited by the number of members available. We present a novel framework involving the application and extension of the CLIMADA open-source climate risk assessment platform, demonstrating an approach for overcoming this limitation. We first show how the CLIMADA platform can be applied to an ensemble of UKCP18 regional climate projections to assess climate risk coherently across space in an idealised example for the UK. We then show how a Generalised Additive Model, involving an ‘ensemble member’ random effect term, can be used to statistically represent the climate model ensemble summary of risk and be used to simulate many more realisations of risk, representative of a larger collection of plausible ensemble members. Specifically, we apply the framework to an idealised example related to heat-stress and the associated risk of reduced outdoor physical working capacity in the UK, based on three global warming levels (recent past, 2 °C and 4 °C warmer than pre-industrial). We show how, in this idealised example, in a 2 °C warmer world (relative to pre-industrial), the UK could lose on average 15 million (or 2.5% of) days of outdoor physical work in a working year (225 days) as a result of heat-stress, which could equate to more than £1.5 billion of economic loss (roughly 0.07% of UK annual GDP). The uncertainty quantification provided by the framework allows for an upper range estimate which better quantifies climate model uncertainty. In a 4 °C warmer world this indicates the plausibility of38 million (or 6.2% of) working days lost in a year, possibly equating to more than £3.8 billion of economic loss (roughly 0.17% of UK annual GDP). Finally, we discuss limitations of the approach and recommend a number of extensions and areas of future work