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During terminal erythropoiesis, the splicing machinery in differentiating erythroblasts executes a robust intron retention (IR) program that impacts expression of hundreds of genes. We studied IR mechanisms in the SF3B1 splicing factor gene, which expresses ∼50% of its transcripts in late erythroblasts as a nuclear isoform that retains intron 4. RNA-seq analysis of nonsense-mediated decay (NMD)-inhibited cells revealed previously undescribed splice junctions, rare or not detected in normal cells, that connect constitutive exons 4 and 5 to highly conserved cryptic cassette exons within the intron. Minigene splicing reporter assays showed that these cassettes promote IR. Genome-wide analysis of splice junction reads demonstrated that cryptic noncoding cassettes are much more common in large (>1 kb) retained introns than they are in small retained introns or in nonretained introns. Functional assays showed that heterologous cassettes can promote retention of intron 4 in the SF3B1 splicing reporter. Although many of these cryptic exons were spliced inefficiently, they exhibited substantial binding of U2AF1 and U2AF2 adjacent to their splice acceptor sites. We propose that these exons function as decoys that engage the intron-terminal splice sites, thereby blocking cross-intron interactions required for excision. Developmental regulation of decoy function underlies a major component of the erythroblast IR program.

The regulation introduced in 2020 that limits the sulfur content in shipping fuel has reduced sulfur emissions over global open oceans by about 80 %. This is expected to have reduced aerosols that both reflect solar radiation directly and affect cloud properties, with the latter also changing the solar radiation balance. Here we investigate the impacts of this regulation on aerosols and climate in the HadGEM3-GC3.1 climate model. The global aerosol effective radiative forcing caused by reduced shipping emissions is estimated to be 0.13 W m-2, which is equivalent to about 50 % of the positive forcing caused by the global reduction in all anthropogenic aerosols since late 20th century. Ensembles of global coupled simulations from 2020–2049 predict a global mean warming of 0.04 K averaged over this period. Our simulations are not clear on whether the global impact is yet to emerge or has already emerged because the present-day impact is masked by variability. Nevertheless, the impact of shipping emission reductions will have either already committed us to warming above the 1.5 K Paris target or will represent an important contribution that may help explain part of the rapid jump in global temperatures over the last 12 months. Consistent with previous aerosol perturbation simulations, the warming is greatest in the Arctic, reaching a mean of 0.15 K Arctic-wide and 0.3 K in the Atlantic sector of the Arctic (which represents greater than 10 % increase in the total anthropogenic warming since pre-industrial times). [ABSTRACT FROM AUTHOR]

The regulation introduced in 2020 that limits the sulfur content in shipping fuel has reduced sulfur emissions over global open oceans by about 80%. This is expected to have reduced aerosols that both reflect solar radiation directly and affect cloud properties, with the latter also changing the solar radiation balance. Here we investigate the impacts of this regulation on aerosols and climate in the HadGEM3-GC3.1 climate model. The global aerosol effective radiative forcing caused by reduced shipping emissions is estimated to be 0.13 W m-2, which is equivalent to about 50% of the positive forcing caused by the global reduction in all anthropogenic aerosols since late 20th century. Ensembles of global coupled simulations from 2020-2049 predict a global mean warming of 0.04 K averaged over this period. Our simulations are not clear on whether the global impact is yet to emerge or has already emerged because the present-day impact is masked by variability. Nevertheless, the impact of shipping emission reductions will have either already committed us to warming above the 1.5 K Paris target or will represent an important contribution that may help explain part of the rapid jump in global temperatures over the last 12 months. Consistent with previous aerosol perturbation simulations, the warming is greatest in the Arctic, reaching a mean of 0.15 K Arctic-wide and 0.3 K in the Atlantic sector of the Arctic (which represents greater than 10% increase in the total anthropogenic warming since pre-industrial times). [ABSTRACT FROM AUTHOR]

We review how the international modelling community, encompassing Integrated Assessment models, global and regional Earth system and climate models, and impact models, have worked together over the past few decades, to advance understanding of Earth system change and its impacts on society and the environment, and support international climate policy. We then recommend a number of priority research areas for the coming ~6 years (i.e. until ~2030), a timescale that matches a number of newly starting international modelling activities and encompasses the IPCC 7th Assessment Report (AR7) and the 2nd UNFCCC Global Stocktake. Progress in these areas will significantly advance our understanding of Earth system change and its impacts and increase the quality and utility of science support to climate policy. We emphasize the need for continued improvement in our understanding of, and ability to simulate, the coupled Earth system and the impacts of Earth system change. There is an urgent need to investigate plausible pathways and emission scenarios that realize the Paris Climate Targets, including pathways that overshoot the 1.5 °C and 2 °C targets, before later returning to them. Earth System models (ESMs) need to be capable of thoroughly assessing such warming overshoots, in particular, the efficacy of negative CO2 emission actions in reducing atmospheric CO2 and driving global cooling. An improved assessment of the long-term consequences of stabilizing climate at 1.5 °C or 2 °C above pre-industrial temperatures is also required. We recommend ESMs run overshoot scenarios in CO2-emission mode, to more fully represent coupled climate - carbon cycle feedbacks. Regional downscaling and impact models should also use forcing data from these simulations, so impact and regional climate projections are as realistic as possible. An accurate simulation of the observed record remains a key requirement of models, as does accurate simulation of key metrics, such as the Effective Climate Sensitivity. For adaptation, improved guidance on potential changes in climate extremes and the modes of variability these extremes develop in, is a key demand. Such improvements will most likely be realized through a combination of increased model resolution and improvement of key parameterizations. We propose a deeper collaboration across modelling efforts targeting increased process realism and coupling, enhanced model resolution, parameterization improvement, and data-driven Machine Learning methods. With respect to sampling future uncertainty, increased collaboration between approaches that emphasize large model ensembles and those focussed on statistical emulation is required. We recommend increased attention is paid to High Impact Low Likelihood (HILL) outcomes. In particular, the risk and consequences of exceeding critical tipping points during a warming overshoot. For a comprehensive assessment of the impacts of Earth system change, including impacts arising directly from specific mitigation actions, it is important detailed, disaggregated information from the Integrated Assessment Models (IAMs) used to generate future scenarios is available to impact models. Conversely, methods need to be developed to incorporate potential future societal responses to the impacts of Earth system change into scenario development. Finally, the new models, simulations, data, and scientific advances, proposed in this article will not be possible without long-term development and maintenance of a robust, globally connected infrastructure ecosystem. This system must be easily accessible and useable across all modelling communities and across the world, allowing the global research community to be fully engaged in developing and delivering new scientific knowledge to support international climate policy. [ABSTRACT FROM AUTHOR]

The role of the land carbon cycle in climate change remains highly uncertain. A key source of the projection spread is related to the assumed response of photosynthesis to warming, especially in the tropics. The optimum temperature for photosynthesis determines whether warming positively or negatively impacts photosynthesis, thereby amplifying or suppressing CO 2 fertilisation of photosynthesis under CO 2 -induced global warming. Land carbon cycle models have been extensively calibrated against local eddy flux measurements, but this has not previously been clearly translated into a reduced uncertainty in terms of how the tropical land carbon sink will respond to warming. Using a previous parameter perturbation ensemble carried out with version 3 of the Hadley Centre coupled climate–carbon cycle model (HadCM3C), we identify an emergent relationship between the optimal temperature for photosynthesis, which is especially relevant in tropical forests, and the projected amount of atmospheric CO 2 at the end of the century. We combine this with a constraint on the optimum temperature for photosynthesis, derived from eddy covariance measurements using the adjoint of the Joint UK Land Environment Simulator (JULES) land surface model. Taken together, the emergent relationship from the coupled model and the constraint on the optimum temperature for photosynthesis define an emergent constraint on future atmospheric CO 2 in the HadCM3C coupled climate–carbon cycle under a common emissions scenario (A1B). The emergent constraint sharpens the probability density of simulated CO 2 change (2100–1900) and moves its peak to a lower value of 497 ± 91 compared to 607 ± 128 ppmv (parts per million by volume) when using the equal-weight prior. Although this result is likely to be model and scenario dependent, it demonstrates the potential of combining the large-scale emergent constraint approach with a parameter estimation using detailed local measurements. [ABSTRACT FROM AUTHOR]

We have created a performance-based assessment of CMIP6 models for Europe that can be used to inform the sub-selection of models for this region. Our assessment covers criteria indicative of the ability of individual models to capture a range of large-scale processes that are important for the representation of present-day European climate. We use this study to provide examples of how this performance-based assessment may be applied to a multi-model ensemble of CMIP6 models to (a) filter the ensemble for performance against these climatological and processed-based criteria and (b) create a smaller subset of models based on performance that also maintains model diversity and the filtered projection range as far as possible. Filtering by excluding the least-realistic models leads to higher-sensitivity models remaining in the ensemble as an emergent consequence of the assessment. This results in both the 25th percentile and the median of the projected temperature range being shifted towards greater warming for the filtered set of models. We also weight the unfiltered ensemble against global trends. In contrast, this shifts the distribution towards less warming. This highlights a tension for regional model selection in terms of selection based on regional climate processes versus the global mean warming trend. [ABSTRACT FROM AUTHOR]

We have created a performance-based assessment of CMIP6 models for Europe that can be used to inform the subselection of models for this region. Our assessment covers criteria indicative of the ability of individual models to capture range of large-scale processes that are important for the representation of present-day European climate. We use this study to provide examples of how this performance-based assessment may be applied to multi-model ensemble of CMIP6 models to a) filter the ensemble for performance against these climatological/processed-based criteria and, b) create a smaller sub-set of models based on performance, that also maintains model diversity and the filtered projection range as far as possible. Filtering by excluding the least realistic models leads to higher sensitivity models remaining in the ensemble as an emergent consequence of the assessment. This results in both the 25th percentile and the median of the projected temperature range being shifted toward greater warming for the filtered set of models. We also weight the unfiltered ensemble against global trends. In contrast this shifts both the distribution of towards less warming. This highlights a tension for regional model selection in terms of selection based on regional climate processes versus the global mean warming trend. [ABSTRACT FROM AUTHOR]

The release of carbon from tropical forests may exacerbate future climate change (1), but the magnitude of the effect in climate models remains uncertain (2). Coupled climate-carbon-cycle models generally agree [...]