Ocean carbon and nitrogen isotopes in CSIRO Mk3L-COAL version 1.0: a tool for palaeoceanographic research

The isotopes of carbon (δ13C) and nitrogen (δ15N) are commonly used proxies for understanding the ocean. When used in tandem, they provide powerful insight into physical and biogeochemical processes. Here, we detail the implementation of δ13C and δ15N in the ocean component of an Earth system model. We evaluate our simulated δ13C and δ15N against contemporary measurements, place the model's performance alongside other isotope-enabled models and document the response of δ13C and δ15N to changes in ecosystem functioning. The model combines the Commonwealth Scientific and Industrial Research Organisation Mark 3L (CSIRO Mk3L) climate system model with the Carbon of the Ocean, Atmosphere and Land (COAL) biogeochemical model. The oceanic component of CSIRO Mk3L-COAL has a resolution of 1.6∘ latitude&thinsp;×&thinsp;2.8∘ longitude and resolves multimillennial timescales, running at a rate of ∼400 years per day. We show that this coarse-resolution, computationally efficient model adequately reproduces water column and core-top δ13C and δ15N measurements, making it a useful tool for palaeoceanographic research. Changes to ecosystem function involve varying phytoplankton stoichiometry, varying CaCO3 production based on calcite saturation state and varying N2 fixation via iron limitation. We find that large changes in CaCO3 production have little effect on δ13C and δ15N, while changes in N2 fixation and phytoplankton stoichiometry have substantial and complex effects. Interpretations of palaeoceanographic records are therefore open to multiple lines of interpretation where multiple processes imprint on the isotopic signature, such as in the tropics, where denitrification, N2 fixation and nutrient utilisation influence δ15N. Hence, there is significant scope for isotope-enabled models to provide more robust interpretations of the proxy records.</p

Recent warming on Spitsbergen - influence of atmospheric circulation and sea ice cover

Spitsbergen has experienced some of the most severe temperature changes in the Arctic during the last three decades. This study relates the recent warming to variations in large-scale atmospheric circulation (AC), air mass characteristics, and sea ice concentration (SIC), both regionally around Spitsbergen and locally in three fjords. We find substantial warming for all AC patterns for all seasons, with greatest temperature increase in winter. A major part of the warming can be attributed to changes in air mass characteristics associated with situations of both cyclonic and anticyclonic air advection from north and east and situations with a nonadvectional anticyclonic ridge. In total, six specific AC types (out of 21), which occur on average 41% of days in a year, contribute approximately 80% of the recent warming. The relationship between the land-based surface air temperature (SAT) and local and regional SIC was highly significant, particularly for the most contributing AC types. The high correlation between SAT and SIC for air masses from east and north of Spitsbergen suggests that a major part of the atmospheric warming observed in Spitsbergen is driven by heat exchange from the larger open water area in the Barents Sea and region north of Spitsbergen. Finally, our results show that changes in frequencies of AC play a minor role to the total recent surface warming. Thus, the strong warming in Spitsbergen in the latest decades is not driven by increased frequencies of “warm” AC types but rather from sea ice decline, higher sea surface temperatures, and a general background warming

Using urine to diagnose large-scale mtDNA deletions in adult patients

Objective: The aim of this study was to evaluate if urinary sediment cells offered a robust alternative to muscle biopsy for the diagnosis of single mtDNA deletions. Methods: Eleven adult patients with progressive external ophthalmoplegia and a known single mtDNA deletion were investigated. Urinary sediment cells were used to isolate DNA, which was then subjected to long-range polymerase chain reaction. Where available, the patient's muscle DNA was studied in parallel. Breakpoint and thus deletion size were identified using both Sanger sequencing and next generation sequencing. The level of heteroplasmy was determined using quantitative polymerase chain reaction. Results: We identified the deletion in urine in 9 of 11 cases giving a sensitivity of 80%. Breakpoints and deletion size were readily detectable in DNA extracted from urine. Mean heteroplasmy level in urine was 38% +/- 26 (range 8 - 84%), and 57% +/- 28 (range 12 - 94%) in muscle. While the heteroplasmy level in urinary sediment cells differed from that in muscle, we did find a statistically significant correlation between these two levels (R = 0.714, P = 0.031(Pearson correlation)). Interpretation: Our findings suggest that urine can be used to screen patients suspected clinically of having a single mtDNA deletion. Based on our data, the use of urine could considerably reduce the need for muscle biopsy in this patient group.Peer reviewe

Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction

Dynamical weather and climate prediction models underpin many studies of the Earth system and hold the promise of being able to make robust projections of future climate change based on physical laws. However, simulations from these models still show many differences compared with observations. Machine learning has been applied to solve certain prediction problems with great success, and recently it's been proposed that this could replace the role of physically-derived dynamical weather and climate models to give better quality simulations. Here, instead, a framework using machine learning together with physically-derived models is tested, in which it is learnt how to correct the errors of the latter from timestep to timestep. This maintains the physical understanding built into the models, whilst allowing performance improvements, and also requires much simpler algorithms and less training data. This is tested in the context of simulating the chaotic Lorenz '96 system, and it is shown that the approach yields models that are stable and that give both improved skill in initialised predictions and better long-term climate statistics. Improvements in long-term statistics are smaller than for single time-step tendencies, however, indicating that it would be valuable to develop methods that target improvements on longer time scales. Future strategies for the development of this approach and possible applications to making progress on important scientific problems are discussed.Comment: 26p, 7 figures To be published in Journal of Advances in Modeling Earth System

High-resolution projections of surface water availability for Tasmania, Australia

Changes to streamflows caused by climate change may have major impacts on the management of water for hydro-electricity generation and agriculture in Tasmania, Australia. We describe changes to Tasmanian surface water availability from 1961–1990 to 2070–2099 using high-resolution simulations. Six fine-scale (&amp;sim;10 km&lt;sup&gt;2&lt;/sup&gt;) simulations of daily rainfall and potential evapotranspiration are generated with the CSIRO Conformal Cubic Atmospheric Model (CCAM), a variable-resolution regional climate model (RCM). These variables are bias-corrected with quantile mapping and used as direct inputs to the hydrological models AWBM, IHACRES, Sacramento, SIMHYD and SMAR-G to project streamflows. &lt;br&gt;&lt;br&gt; The performance of the hydrological models is assessed against 86 streamflow gauges across Tasmania. The SIMHYD model is the least biased (median bias = −3%) while IHACRES has the largest bias (median bias = −22%). We find the hydrological models that best simulate observed streamflows produce similar streamflow projections. &lt;br&gt;&lt;br&gt; There is much greater variation in projections between RCM simulations than between hydrological models. Marked decreases of up to 30% are projected for annual runoff in central Tasmania, while runoff is generally projected to increase in the east. Daily streamflow variability is projected to increase for most of Tasmania, consistent with increases in rainfall intensity. Inter-annual variability of streamflows is projected to increase across most of Tasmania. &lt;br&gt;&lt;br&gt; This is the first major Australian study to use high-resolution bias-corrected rainfall and potential evapotranspiration projections as direct inputs to hydrological models. Our study shows that these simulations are capable of producing realistic streamflows, allowing for increased confidence in assessing future changes to surface water variability

Capabilities of Global Ocean Programmes to Inform Climate Services

AbstractClimate services are identified as a means of providing the information that is needed to support decision makers in assessing the impacts of climate change on the oceans. We discuss the current observation programs to support these services, and their capacity to provide the information needed to monitor and address key science questions. An analysis of the current oceanographic observation programs is shown to be undersubscribed from their original plans. There are vulnerabilities in the current observing programs, particularly in relation to satellite measurements. The interaction of climate services with the research community, with policy makers and stakeholders and operational centres is outlined and leads to four recommendations. The key recommendations are for the more pervasisve development of climate services and for a modest increment in the observing program informed by the recommendations of the OceanObs’09 conference

Attributing human mortality during extreme heat waves to anthropogenic climate change

It has been argued that climate change is the biggest global health threat of the 21st century. The extreme high temperatures of the summer of 2003 were associated with up to seventy thousand excess deaths across Europe. Previous studies have attributed the meteorological event to the human influence on climate, or examined the role of heat waves on human health. Here, for the first time, we explicitly quantify the role of human activity on climate and heat-related mortality in an event attribution framework, analysing both the Europe-wide temperature response in 2003, and localised responses over London and Paris. Using publicly-donated computing, we perform many thousands of climate simulations of a high-resolution regional climate model. This allows generation of a comprehensive statistical description of the 2003 event and the role of human influence within it, using the results as input to a health impact assessment model of human mortality. We find large-scale dynamical modes of atmospheric variability remain largely unchanged under anthropogenic climate change, and hence the direct thermodynamical response is mainly responsible for the increased mortality. In summer 2003, anthropogenic climate change increased the risk of heat-related mortality in Central Paris by ~70% and by ~20% in London, which experienced lower extreme heat. Out of the estimated ~315 and ~735 summer deaths attributed to the heatwave event in Greater London and Central Paris, respectively, 64 (±3) deaths were attributable to anthropogenic climate change in London, and 506 (±51) in Paris. Such an ability to robustly attribute specific damages to anthropogenic drivers of increased extreme heat can inform societal responses to, and responsibilities for, climate change

Chapter 10 - Detection and attribution of climate change: From global to regional

This chapter assesses the causes of observed changes assessed in Chapters 2 to 5 and uses understanding of physical processes, climate models and statistical approaches. The chapter adopts the terminology for detection and attribution proposed by the IPCC good practice guidance paper on detection and attribution (Hegerl et al., 2010) and for uncertainty Mastrandrea et al. (2011). Detection and attribution of impacts of climate changes are assessed by Working Group II, where Chapter 18 assesses the extent to which atmospheric and oceanic changes influence ecosystems, infrastructure, human health and activities in economic sectors