Theoretical foundations of emergent constraints: relationships between climate sensitivity and global temperature variability in conceptual models

This is the final version. Available on open access from OUP via the DOI in this recordBackground: The emergent constraint approach has received interest recently as a way of utilizing multimodel General Circulation Model (GCM) ensembles to identify relationships between observable variations of climate and future projections of climate change. These relationships, in combination with observations of the real climate system, can be used to infer an emergent constraint on the strength of that future projection in the real system. However, there is as yet no theoretical framework to guide the search for emergent constraints. As a result, there are significant risks that indiscriminate data-mining of the multidimensional outputs from GCMs could lead to spurious correlations and less than robust constraints on future changes. To mitigate against this risk, Cox et al (hereafter CHW18) proposed a theory-motivated emergent constraint, using the one-box Hasselmann model to identify a linear relationship between equilibrium climate sensitivity (ECS) and a metric of global temperature variability involving both temperature standard deviation and autocorrelation (Ψ). A number of doubts have been raised about this approach, some concerning the application of the one-box model to understand relationships in complex GCMs which are known to have more than the single characteristic timescale. Objectives: To study whether the linear Ψ-ECS proportionality in CHW18 is an artefact of the one-box model. More precisely we ask ‘Does the linear Ψ-ECS relationship feature in the more complex and realistic two-box and diffusion models?’. Methods: We solve the two-box and diffusion models to find relationships between ECS and Ψ. These models are forced continually with white noise parameterizing internal variability. The resulting analytical relations are essentially fluctuation-dissipation theorems. Results: We show that the linear Ψ-ECS proportionality in the one-box model is not generally true in the two-box and diffusion models. However, the linear proportionality is a very good approximation for parameter ranges applicable to the current state-of-the-art CMIP5 climate models. This is not obvious - due to structural differences between the conceptual models, their predictions also differ. For example, the two-box and diffusion, unlike the one-box model, can reproduce the long term transient behaviour of the CMIP5 abrupt4xCO2 and 1pcCO2 simulations. Each of the conceptual models also predict different power spectra with only the diffusion model’s pink 1/f spectrum being compatible with observations and GCMs. We also show that the theoretically predicted Ψ-ECS relationship exists in the piControl as well as historical CMIP5 experiments and that the differing gradients of the proportionality are inversely related to the effective forcing in that experiment. Conclusions: We argue that emergent constraints should ideally be derived by such theory-driven hypothesis testing, in part to protect against spurious correlations from blind data-mining but mainly to aid understanding. In this approach, an underlying model is proposed, the model is used to predict a potential emergent relationship between an observable and an unknown future projection, and the hypothesised emergent relationship is tested against an ensemble of GCMs.European Union Horizon 2020Engineering and Physical Sciences Research Council (EPSRC)European Research Counci

Emergent Constraints on Climate-Carbon Cycle Feedbacks

This is the final version. Available on open access from Springer via the DOI in this recordPurpose of Review: Feedbacks between CO2-induced climate change and the carbon cycle are now routinely represented in the Earth System Models (ESMs) that are used to make projections of future climate change. The inconclusion of climate-carbon cycle feedbacks in climate projections is an important advance, but has added a significant new source of uncertainty. This review assesses the potential for emergent constraints to reduce the uncertainties associated with climate-carbon cycle feedbacks. Recent Findings: The emergent constraint technique involves using the full ensemble of models to find an across-ensemble relationship between an observable feature of the Earth System (such as a trend, interannual variation or change in seasonality) and an uncertain aspect of the future. Examples focussing on reducing uncertainties in future atmospheric CO2 concentration, carbon loss from tropical land under warming and CO2 fertilization of mid- and high-latitude photosynthesis are exemplars of these different types of emergent constraints. Summary: The power of emergent constraints is that they use the enduring range in model projections to reduce uncertainty in the future of the real Earth System, but there are also risks that indiscriminate data-mining, and systematic model errors could yield misleading constraints. A hypothesis-driven theory-led approach can overcome these risks and also reveal the true promise of emergent constraints—not just as ways to reduce uncertainty in future climate change but also to catalyse advances in our understanding of the Earth System.European Research Council (ERC)European Union Horizon 202

Land-surface parameter optimisation using data assimilation techniques: The adJULES system V1.0

This is the final version of the article. Available from the European Geosciences Union (EGU) via the DOI in this record.Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate-carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. This paper describes adJULES in a data assimilation framework and demonstrates its ability to improve the model-data fit using eddy-covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85% of the sites used in the study, at both the calibration and evaluation stages. The new improved parameters for JULES are presented along with the associated uncertainties for each parameter.This work was supported by the UK Natural Environment Research Council (NERC) through the National Centre for Earth Observation (NCEO). This study used eddy-covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOSSiberia, USCCC. Support for eddy-covariance data harmonisation was provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California – Berkeley, University of Virginia. The authors are grateful to T. Kaminski and R. Giering from FastOpt for their contribution to the development of the adjoint model, and to M. Groenendijk, A. Harper, and the UK Met Office for processing and sharing their data. The authors are particularly grateful to two anonymous referees for their thoughtful and constructive reviews, which greatly improved this manuscript

Spatial and temporal variations in plant water-use efficiency inferred from tree-ring, eddy covariance and atmospheric observations

This is the final version of the article. Available from the European Geosciences Union (EGU) via the DOI in this record.Plant water-use efficiency (WUE), which is the ratio of the uptake of carbon dioxide through photosynthesis to the loss of water through transpiration, is a very useful metric of the functioning of the land biosphere. WUE is expected to increase with atmospheric CO2, but to decline with increasing atmospheric evaporative demand – which can arise from increases in near-surface temperature or decreases in relative humidity. We have used Δ13C measurements from tree rings, along with eddy covariance measurements from Fluxnet sites, to estimate the sensitivities of WUE to changes in CO2 and atmospheric humidity deficit. This enables us to reconstruct fractional changes in WUE, based on changes in atmospheric climate and CO2, for the entire period of the instrumental global climate record. We estimate that overall WUE increased from 1900 to 2010 by 48±22%, which is more than double that simulated by the latest Earth System Models. This long-term trend is largely driven by increases in CO2, but significant inter-annual variability and regional differences are evident due to variations in temperature and relative humidity. There are several highly populated regions, such as western Europe and East Asia, where the rate of increase of WUE has declined sharply in the last 2 decades. Our data-based analysis indicates increases in WUE that typically exceed those simulated by Earth System Models-implying that these models are either underestimating increases in photosynthesis or underestimating reductions in transpiration.The contributions of M. Groenendijk, C. Huntingford, and P. M. Cox were funded by the UK Natural Environment Research Council (NERC) HYDRA project. We thank Margaret Barbour for providing tree-ring data compiled from many different sources. This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California – Berkeley, University of Virginia

Indigenous microbial surrogates in wastewater used to understand public health risk expressed in the Disability-Adjusted Life Year (DALY) metric

In any wastewater recycling scheme, the protection of public health is of primary importance. In Australia, the public health requirements applying to the treatment of recycled water are stringent. They use the Disability-Adjusted Life Year (DALY) metric to set a level of negligible public health risk. The target maximum risk of 10-6 DALY per person per year has been adopted in Australian water recycling guidelines since 2006. A key benefit of the DALY approach is its ability to standardise the understanding of risk across disparate areas of public health. To address the key challenge of translating the results of monitoring of microorganisms in the recycled water into this quantitative public health metric, we have developed a novel method. This paper summarises an approach where microbial surrogate organisms indigenous to wastewater are used to measure the efficiency of water recycling treatment processes and estimate public health risk. An example of recent implementation in the Greater Sydney region of Australia is provided

An observation-based constraint on permafrost loss as a function of global warming

This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this recordPermafrost, which covers 15 million km 2 of the land surface, is one of the components of the Earth system that is most sensitive to warming. Loss of permafrost would radically change high-latitude hydrology and biogeochemical cycling, and could therefore provide very significant feedbacks on climate change. The latest climate models all predict warming of high-latitude soils and thus thawing of permafrost under future climate change, but with widely varying magnitudes of permafrost thaw. Here we show that in each of the models, their present-day spatial distribution of permafrost and air temperature can be used to infer the sensitivity of permafrost to future global warming. Using the same approach for the observed permafrost distribution and air temperature, we estimate a sensitivity of permafrost area loss to global mean warming at stabilization of million km 2 °C â '1 (1σ confidence), which is around 20% higher than previous studies. Our method facilitates an assessment for COP21 climate change targets: if the climate is stabilized at 2 °C above pre-industrial levels, we estimate that the permafrost area would eventually be reduced by over 40%. Stabilizing at 1.5 °C rather than 2 °C would save approximately 2 million km 2 of permafrost.European Union Seventh Framework ProgrammeNatural Environment Research Council (NERC)Swedish Research CouncilResearch Council of NorwayUK DECC/Defra Met Office HadleyEuropean Unio

Evidence of localised Amazon rainforest dieback in CMIP6 models

This is the final version. Available on open access from th European Geosciences Union via the DOI in this recordCode and data availability: The CMIP6 model output datasets analysed during this study are available online at https://doi.org/10.22033/ESGF/CMIP6.4507 (EC-Earth Consortium, 2019), https://doi.org/10.22033/ESGF/CMIP6.8473 (Krasting et al., 2018), doi10.22033/ESGF/CMIP6.6435 (Wieners et al., 2019), https://doi.org/10.22033/ESGF/CMIP6.10861 (Bethke et al., 2019), https://doi.org/10.22033/ESGF/CMIP6.7782 (Park and Shin, 2019), https://doi.org/10.22033/ESGF/CMIP6.9702 (Lee and Liang, 2020), and https://doi.org/10.22033/ESGF/CMIP6.5792 (Tang et al., 2019). Code used for analysis is available at https://doi.org/10.5281/zenodo.7038389 (Parry et al., 2022).Amazon forest dieback is seen as a potential tipping point under climate change. These concerns are partly based on an early coupled climate–carbon cycle simulation that produced unusually strong drying and warming in Amazonia. In contrast, the fifth-generation Earth system models (Phase 5 of the Coupled Model Intercomparison Project, CMIP5) produced few examples of Amazon dieback under climate change. Here we examine results from seven sixth-generation models (Phase 6 of the Coupled Model Intercomparison Project, CMIP6), which include interactive vegetation carbon and in some cases interactive forest fires. Although these models typically project increases in area-mean forest carbon across Amazonia under CO2-induced climate change, five of the seven models also produce abrupt reductions in vegetation carbon, which indicate localised dieback events. The northern South America (NSA) region, which contains most of the rainforest, is especially vulnerable in the models. These dieback events, some of which are mediated by fire, are preceded by an increase in the amplitude of the seasonal cycle in near-surface temperature, which is consistent with more extreme dry seasons. Based on the ensemble mean of the detected dieback events we estimate that 7±5 % of the NSA region will experience abrupt downward shifts in vegetation carbon for every degree of global warming past 1.5 ∘C.European Research Council (ERC)European Union Horizon 2020Engineering and Physical Sciences Research Council (EPSRC

Emergent constraints on projections of declining primary production in the tropical oceans

This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this recordMarine primary production is a fundamental component of the Earth system, providing the main source of food and energy to the marine food web, and influencing the concentration of atmospheric CO 2 (refs,). Earth system model (ESM) projections of global marine primary production are highly uncer tain with models projecting both increases and declines of up to 20% by 2100. This uncertainty is predominantly driven by the sensitivity of tropical ocean primary production to climate change, with the latest ESMs suggesting twenty-first-century tropical declines of between 1 and 30% (refs,). Here we identify an emergent relationship between the long-term sensitivity of tropical ocean primary production to rising equatorial zone sea surface temperature (SST) and the interannual sensitivity of primary production to El Niño/Southern Oscillation (ENSO)-driven SST anomalies. Satellite-based observations of the ENSO sensitivity of tropical primary production are then used to constrain projections of the long-term climate impact on primary production. We estimate that tropical primary production will decline by 3 ± 1% per kelvin increase in equatorial zone SST. Under a business-as-usual emissions scenario this results in an 11 ± 6% decline in tropical marine primary production and a 6 ± 3% decline in global marine primary production by 2100.European Union Horizon 202

Impacts of climate extremes in Brazil the development of a web platform for understanding long-term sustainability of ecosystems and human health in amazonia (pulse-Brazil)

This is the final version of the article. Available from the American Meteorological Society via the DOI in this record.This work was funded by the joint FAPESP 2011/51843-2 and NERC NE/J016276/1 International Opportunities Fund. PULSE-Brazil development is also funded by the FAPESP grant (2012/51876-0) under the Belmont Forum Cooperation Agreement. Marengo and Aragão thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for their Research Productivity Fellowship

Sliding charge density wave in manganites

The so-called stripe phase of the manganites is an important example of the complex behaviour of metal oxides, and has long been interpreted as the localisation of charge at atomic sites. Here, we demonstrate via resistance measurements on La_{0.50}Ca_{0.50}MnO_3 that this state is in fact a prototypical charge density wave (CDW) which undergoes collective transport. Dramatic resistance hysteresis effects and broadband noise properties are observed, both of which are typical of sliding CDW systems. Moreover, the high levels of disorder typical of manganites result in behaviour similar to that of well-known disordered CDW materials. Our discovery that the manganite superstructure is a CDW shows that unusual transport and structural properties do not require exotic physics, but can emerge when a well-understood phase (the CDW) coexists with disorder.Comment: 13 pages; 4 figure