Probabilistic projections of future warming and climate sensitivity trajectories

Projections of future global mean surface warming for a given forcing scenario remain uncertain, largely due to uncertainty in the climate sensitivity. The ensemble of Earth system models from the Climate Model Intercomparison Project phase 6 (CMIP6) represent the dominant tools for projecting future global warming. However, the distribution of climate sensitivities within the CMIP6 ensemble is not representative of recent independent probabilistic estimates, and the ensemble contains significant variation in simulated historic surface warming outside agreement with observational datasets. Here, a Bayesian approach is used to infer joint probabilistic projections of future surface warming and climate sensitivity for SSP scenarios. The projections use an efficient climate model ensemble filtered and weighted to encapsulate observational uncertainty in historic warming and ocean heat content anomalies. The probabilistic projection of climate sensitivity produces a best estimate of 2.9 °C, and 5th to 95th percentile range of 1.5 to 4.6 °C, in line with previous estimates using multiple lines of evidence. The joint projection of surface warming over the period 2030 to 2040 has a 50% or greater probability of exceeding 1.5 °C above preindustrial for all SSPs considered: 119, 126, 245, 370 and 585. Average warming by the period 2050 to 2060 has a greater than 50% chance of exceeding 2 °C for SSPs 245, 370 and 585. These results imply that global warming is no longer likely to remain under 1.5 °C, even with drastic and immediate mitigation, and highlight the importance of urgent action to avoid exceeding 2 °C warming.

Assessing the quality of state-of-the-art regional climate information: the case of the UK Climate Projections 2018

<p>We assess the quality of state-of-the-art regional climate information intended to support adaptation decision-making. We use the UK Climate Projections 2018 (UKCP18) as an example of such information. The probabilistic, global and regional land projections of UKCP18 exemplify some of the key methodologies that are at the forefront of providing regional climate information for decision support in adapting to a changing climate. We assess the quality of the evidence and the methodology used to support their statements about future regional climate derived from these projections along five quality dimensions: transparency, theory, diversity, completeness and adequacy for purpose. The assessment produced two major insights. First, the main issue that taints the quality of UKCP18 is the lack of transparency. The lack of transparency is particularly problematic if the information is directed towards non-expert users, who would need to develop technical skills to evaluate the quality and epistemic reliability of this information. Second, the probabilistic projections are of lower quality than the global projections. This assessment is a consequence of both lack of transparency in the probabilistic projections, and the way the method is used and justified to produce quantified uncertainty estimates about future climate. We suggest how higher quality could be achieved. This can be achieved by improving transparency of evidence and methodology and by better satisfying other dimensions through changes in elements of evidence and methodology. We conclude by recommending further avenues for testing the effectiveness of the framework and highlighting the need for further research in user perspectives on quality.</p>

Multi-sectoral evaluation of climate change risks and adaptation urgency using a response surface approach

<p>This paper presents a new approach to climate change impact and adaptation analysis within a risk framework. We test the feasibility of applying impact models for representing three aspects of potential relevance for policy: (i) sensitivity –  examining the sensitivity of the sectors to changing climate for readily observable indicators; (ii) urgency – estimating risks of approaching or exceeding critical thresholds of impact under alternative scenarios as a basis for determining urgency of response; and (iii) response –  determining the effectiveness of potential adaptation and mitigation responses. By working with observable indicators, the approach is also amenable to long-term monitoring as well as evaluation of the success of adaptation, where this too can be simulated.</p><p> </p><p>The approach involves the construction of impact response surfaces (IRSs) based on impact model simulations, using sectoral impact models that are also capable of simulating some adaptation measures. An IRS is constructed from an analysis of the modelled sensitivity of an impact indicator of interest to systematic changes in key drivers (e.g. temperature and precipitation) and the resulting impact variable is plotted as a surface comprising contour lines of equal response over a wide range of perturbations. This facilitates analysis of model behaviour across many possible future conditions. IRSs can also be combined with probabilistic projections of climate change to estimate the likelihood of exceeding certain critical thresholds of impact. An important step here is the identification of such critical thresholds, which are meaningful limits of tolerance for the functioning of the system and typically requiring expert advice from key stakeholders.</p><p> </p><p>Two examples are shown that illustrate the types of analyses to be undertaken and their potential outputs: risks of crop yield shortfall in Finland (Pirttioja et al. 2019) and impact risks for water management in a Portuguese reservoir (Fronzek et al., in prep.). Three challenges require special attention in this new modelling exercise: (a) ensuring the salience and credibility of the modelling conducted, through engagement with relevant stakeholders, (b) co-exploration of the capabilities of current impact models and the need for improved representation of adaptation and (c) co-identification of critical thresholds for key impact indicators and effective representation of uncertainties.</p><p> </p><p>The approach is currently being tested at national scale in Finland in the Adapt-FIRST project (https://www.syke.fi/projects/adapt-first), using models of water resources, agriculture, forest productivity, nature recreation and human health to address multiple climate-related hazards such as droughts, floods, heat and forest fires and their interaction with mean changes in climate. Impact likelihoods will be estimated for regions in Finland, contributing to a national risk assessment to support adaptation policies. This approach could be a useful device for indicating the level of urgency for action, whether by adaptation to ameliorate the risk or mitigation to avert the hazard.</p><p> </p><p> </p><p>References</p><p>Fronzek et al. (in prep.) Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces.</p><p>Pirttioja et al. (2019) Using impact response surfaces to analyse the likelihood of impacts on crop yield under probabilistic climate change. Agr Forest Meteorol 264:213-224.</p>

Past, Present and Future Sea Levels in Singapore

<p>Singapore is a small (728 km<sup>2</sup>) island nation that is vulnerable to rising sea levels with 30% of its land surface area less than 5 m above present sea level. Rising relative sea level (RSL), however, is not uniform with regional RSL changes differing from the global mean due to processes associated with vertical land motion (e.g., glacial-isostatic adjustment) and atmospheric and ocean dynamics. Understanding magnitudes, rates, and driving processes on past and present-day sea level are therefore important to provide greater confidence in accurately quantifying future sea-level rise projections and their uncertainty. Here, we present a synopsis of Singapore’s past and present RSL history using newly developed proxy RSL reconstructions from mangrove peats, coral microatolls and tide gauge data and conclude with probabilistic projections of future RSL change.</p><p>Past RSL is characterized by rapid rise during the early Holocene driven primarily by deglaciation of northern hemisphere ice sheets. Sea-level index points (SLIPs) from mangrove peats show sea levels rose rapidly from -20.7 m at 9.5 ka BP to -0.6 m at 7 ka BP at rates of 6-12 mm/yr. This is substantially greater than predicted magnitudes of RSL change from the ICE-6G_C GIA model which shows RSL increasing from -6.4 m at 9.5 ka BP to a ~2.8 m highstand at ~7 ka BP. SLIPs show the mid-Holocene highstand of ~4 ± 3.6 m at 5.2 ka BP before falling towards present at rates up to -2 mm/yr driven by hydro-isostatic processes. The nature of RSL changes during the mid- to late-Holocene transition remains poorly resolved with evidence of sea levels falling below present level to -2.2 ± 2.0 m at 1.2 ka BP. Present RSL reconstructions from coral microatolls coupled with tide-gauge data extend the limited instrumental period in this region beyond ~50 years. They show RSL rose ~0.03 m from 1915 to 1990 at 0.7 ± 1.4 mm/yr before increasing to 1.5 ± 2.1 mm/yr after 1990 to 2019. Future RSL change from probabilistic projections to 2100 under low (RCP 2.6) and high (RCP 8.5) emission scenarios show sea levels rising 0.43 m (50<sup>th</sup> percentile) (0.06 – 0.96 m; 95% credible interval) and 0.74 m (0.28 – 1.4 m), respectively. However, projected magnitudes of sea-level rise driven by rapid ice sheet dynamics and the unknown contribution of atmospheric and ocean dynamics in Southeast Asia have the potential to exacerbate projection magnitudes.</p>

UK Climate Projections 2018 (UKCP18): progress towards more information on future weather

<p>UK Climate Projections 2018 (UKCP18) included land and marine projections and were published in 2018 to replace UKCP09. The land projections had three components, and all were designed to provide more information on future weather compared to UKCP09. The first component updated the UKCP09 probabilistic projections by including newer CMIP5 data and focussing on seasonal means from individual years rather than 30-year averages. The probabilistic projections represent the wider uncertainty. The second two components (global and regional projections) both had the aim of providing plausible examples of future climate, but at different resolutions.</p><p>The global projections were a combination of 13 CMIP5 models and a 15-member perturbed parameter ensemble (PPE) of coupled simulations for 1900-2100 using CMIP5 RCP8.5 from 2005 onwards. The PPE was provided at 60km atmosphere, quarter degree ocean and the large-scale conditions from twelve of the members were used to drive the regional model at both 12km and 2.2km resolution. These plausible examples are more useful for providing information about weather in a future climate to support a storyline approach for decision making.</p><p>The talk will present examples of new ways to use UKCP18 compared to UKCP09.  We will show how the global projections can be used to understand that the recent record winter daily maximum temperature in the UK in 2019 had a large contribution from internal variability and what this means for breaking the record in a warming climate. We also use an example from China to demonstrate one way to exploit information at different time scales, looking at how a circulation index, which is predictable and related to tropical cyclone landfall, changes in a future climate.</p><p>Finally, we show that while the enhanced resolution of the global and regional projections has improved our capability to provide climate information linked to the better representation of circulation, they lack diversity in some of the key drivers of future climate. Therefore, a key way forward will be to find an appropriate balance between the need for better diversity (e.g. multiple ensembles such as CMIP or PPEs) and the need for an appropriate resolution to retain this new capability.</p>