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- ItemReply to Bhowmik et al.: Democratic climate action and studying extreme climate risks are not in tension(Washington, DC : National Acad. of Sciences, 2022) Kemp, Luke; Xu, Chi; Depledge, Joanna; Ebi, Kristie L.; Gibbins, Goodwin; Kohler, Timothy A.; Rockström, Johan; Scheffer, Marten; Schellnhuber, Hans Joachim; Steffen, Will; Lenton, Timothy M.[no abstract available]
- ItemMonsoon forced evolution of savanna and the spread of agro-pastoralism in peninsular India([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Riedel, Nils; Fuller, Dorian Q.; Marwan, Norbert; Poretschkin, Constantin; Basavaiah, Nathani; Menzel, Philip; Ratnam, Jayashree; Prasad, Sushma; Sachse, Dirk; Sankaran, Mahesh; Sarkar, Saswati; Stebich, MartinaAn unresolved issue in the vegetation ecology of the Indian subcontinent is whether its savannas, characterized by relatively open formations of deciduous trees in C4-grass dominated understories, are natural or anthropogenic. Historically, these ecosystems have widely been regarded as anthropogenic-derived, degraded descendants of deciduous forests. Despite recent work showing that modern savannas in the subcontinent fall within established bioclimatic envelopes of extant savannas elsewhere, the debate persists, at least in part because the regions where savannas occur also have a long history of human presence and habitat modification. Here we show for the first time, using multiple proxies for vegetation, climate and disturbances from high-resolution, well-dated lake sediments from Lonar Crater in peninsular India, that neither anthropogenic impact nor fire regime shifts, but monsoon weakening during the past ~ 6.0 kyr cal. BP, drove the expansion of savanna at the expense of forests in peninsular India. Our results provide unambiguous evidence for a climate-induced origin and spread of the modern savannas of peninsular India at around the mid-Holocene. We further propose that this savannization preceded and drove the introduction of agriculture and development of sedentism in this region, rather than vice-versa as has often been assumed.
- ItemDecay radius of climate decision for solar panels in the city of Fresno, USA([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Barton-Henry, Kelsey; Wenz, Leonie; Levermann, AndersTo design incentives towards achieving climate mitigation targets, it is important to understand the mechanisms that affect individual climate decisions such as solar panel installation. It has been shown that peer effects are important in determining the uptake and spread of household photovoltaic installations. Due to coarse geographical data, it remains unclear whether this effect is generated through geographical proximity or within groups exhibiting similar characteristics. Here we show that geographical proximity is the most important predictor of solar panel implementation, and that peer effects diminish with distance. Using satellite imagery, we build a unique geo-located dataset for the city of Fresno to specify the importance of small distances. Employing machine learning techniques, we find the density of solar panels within the shortest measured radius of an address is the most important factor in determining the likelihood of that address having a solar panel. The importance of geographical proximity decreases with distance following an exponential curve with a decay radius of 210 meters. The dependence is slightly more pronounced in low-income groups. These findings support the model of distance-related social diffusion, and suggest priority should be given to seeding panels in areas where few exist.
- ItemRegionally aggregated, stitched and de‐drifted CMIP‐climate data, processed with netCDF‐SCM v2.0.0(Chichester [u.a.] : Wiley, 2021) Nicholls, Zebedee; Lewis, Jared; Makin, Melissa; Nattala, Usha; Zhang, Geordie Z.; Mutch, Simon J.; Tescari, Edoardo; Meinshausen, MalteThe world's most complex climate models are currently running a range of experiments as part of the Sixth Coupled Model Intercomparison Project (CMIP6). Added to the output from the Fifth Coupled Model Intercomparison Project (CMIP5), the total data volume will be in the order of 20PB. Here, we present a dataset of annual, monthly, global, hemispheric and land/ocean means derived from a selection of experiments of key interest to climate data analysts and reduced complexity climate modellers. The derived dataset is a key part of validating, calibrating and developing reduced complexity climate models against the behaviour of more physically complete models. In addition to its use for reduced complexity climate modellers, we aim to make our data accessible to other research communities. We facilitate this in a number of ways. Firstly, given the focus on annual, monthly, global, hemispheric and land/ocean mean quantities, our dataset is orders of magnitude smaller than the source data and hence does not require specialized ‘big data’ expertise. Secondly, again because of its smaller size, we are able to offer our dataset in a text-based format, greatly reducing the computational expertise required to work with CMIP output. Thirdly, we enable data provenance and integrity control by tracking all source metadata and providing tools which check whether a dataset has been retracted, that is identified as erroneous. The resulting dataset is updated as new CMIP6 results become available and we provide a stable access point to allow automated downloads. Along with our accompanying website (cmip6.science.unimelb.edu.au), we believe this dataset provides a unique community resource, as well as allowing non-specialists to access CMIP data in a new, user-friendly way.
- ItemRecurrence flow measure of nonlinear dependence(Berlin ; Heidelberg : Springer, 2022) Braun, Tobias; Kraemer, K. Hauke; Marwan, NorbertCouplings in complex real-world systems are often nonlinear and scale dependent. In many cases, it is crucial to consider a multitude of interlinked variables and the strengths of their correlations to adequately fathom the dynamics of a high-dimensional nonlinear system. We propose a recurrence-based dependence measure that quantifies the relationship between multiple time series based on the predictability of their joint evolution. The statistical analysis of recurrence plots (RPs) is a powerful framework in nonlinear time series analysis that has proven to be effective in addressing many fundamental problems, e.g., regime shift detection and identification of couplings. The recurrence flow through an RP exploits artifacts in the formation of diagonal lines, a structure in RPs that reflects periods of predictable dynamics. Using time-delayed variables of a deterministic uni-/multivariate system, lagged dependencies with potentially many time scales can be captured by the recurrence flow measure. Given an RP, no parameters are required for its computation. We showcase the scope of the method for quantifying lagged nonlinear correlations and put a focus on the delay selection problem in time-delay embedding which is often used for attractor reconstruction. The recurrence flow measure of dependence helps to identify non-uniform delays and appears as a promising foundation for a recurrence-based state space reconstruction algorithm.
- ItemReply to Burgess et al: Catastrophic climate risks are neglected, plausible, and safe to study(Washington, DC : National Acad. of Sciences, 2022) Kemp, Luke; Xu, Chi; Depledge, Joanna; Ebi, Kristie L.; Gibbins, Goodwin; Kohler, Timothy A.; Rockström, Johan; Scheffer, Marten; Schellnhuber, Hans Joachim; Steffen, Will; Lenton, Timothy M.
- ItemPacific climate reflected in Waipuna Cave drip water hydrochemistry(Munich : EGU, 2020) Nava-Fernandez, Cinthya; Hartland, Adam; Gázquez, Fernando; Kwiecien, Ola; Marwan, Norbert; Fox, Bethany; Hellstrom, John; Pearson, Andrew; Ward, Brittany; French, Amanda; Hodell, David A.; Immenhauser, Adrian; Breitenbach, Sebastian F.M.Cave microclimate and geochemical monitoring is vitally important for correct interpretations of proxy time series from speleothems with regard to past climatic and environmental dynamics. We present results of a comprehensive cave-monitoring programme in Waipuna Cave in the North Island of New Zealand, a region that is strongly influenced by the Southern Westerlies and the El Niño-Southern Oscillation (ENSO). This study aims to characterise the response of the Waipuna Cave hydrological system to atmospheric circulation dynamics in the southwestern Pacific region in order to assure the quality of ongoing palaeo-environmental reconstructions from this cave. Drip water from 10 drip sites was collected at roughly monthly intervals for a period of ca. 3 years for isotopic (d18O, dD, d-excess parameter, d17O, and 17Oexcess) and elemental (Mg=Ca and Sr=Ca) analysis. The monitoring included spot measurements of drip rates and cave air CO2 concentration. Cave air temperature and drip rates were also continuously recorded by automatic loggers. These datasets were compared to surface air temperature, rainfall, and potential evaporation from nearby meteorological stations to test the degree of signal transfer and expression of surface environmental conditions in Waipuna Cave hydrochemistry. Based on the drip response dynamics to rainfall and other characteristics, we identified three types of discharge associated with hydrological routing in Waipuna Cave: (i) type 1-diffuse flow, (ii) type 2-fracture flow, and (iii) type 3-combined flow. Drip water isotopes do not reflect seasonal variability but show higher values during severe drought. Drip water d18O values are characterised by small variability and reflect the mean isotopic signature of precipitation, testifying to rapid and thorough homogenisation in the epikarst. Mg=Ca and Sr=Ca ratios in drip waters are predominantly controlled by prior calcite precipitation (PCP). Prior calcite precipitation is strongest during austral summer (December-February), reflecting drier conditions and a lack of effec tive infiltration, and is weakest during the wet austral winter (July-September). The Sr=Ca ratio is particularly sensitive to ENSO conditions due to the interplay of congruent or incongruent host rock dissolution, which manifests itself in lower Sr=Ca in above-average warmer and wetter (La Niña-like) conditions. Our microclimatic observations at Waipuna Cave provide a valuable baseline for the rigorous interpretation of speleothem proxy records aiming at reconstructing the past expression of Pacific climate modes. © 2020 Author(s).
- ItemReply to Kelman: The foundations for studying catastrophic climate risks(Washington, DC : National Acad. of Sciences, 2022) Kemp, Luke; Xu, Chi; Depledge, Joanna; Ebi, Kristie L.; Gibbins, Goodwin; Kohler, Timothy A.; Rockström, Johan; Scheffer, Marten; Schellnhuber, Hans Joachim; Steffen, Will; Lenton, Timothy M.
- ItemReduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections(Hoboken, NJ : Wiley-Blackwell, 2021) Nicholls, Z.; Meinshausen, M.; Lewis, J.; Corradi, M. Rojas; Dorheim, K.; Gasser, T.; Gieseke, R.; Hope, A.P.; Leach, N.J.; McBride, L.A.; Quilcaille, Y.; Rogelj, J.; Salawitch, R.J.; Samset, B.H.; Sandstad, M.; Shiklomanov, A.; Skeie, R.B.; Smith, C.J.; Smith, S.J.; Su, X.; Tsutsui, J.; Vega-Westhoff, B.; Woodard, D.L.Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state-of-the-art knowledge in an internally self-consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850-1900, using an observationally based historical warming estimate of 0.8°C between 1850-1900 and 1995-2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.
- ItemWFDE5: Bias-adjusted ERA5 reanalysis data for impact studies(Katlenburg-Lindau : Copernics Publications, 2020) Cucchi, Marco; Weedon, Graham P.; Amici, Alessandro; Bellouin, Nicolas; Lange, Stefan; Müller Schmied, Hannes; Hersbach, Hans; Buontempo, CarloThe WFDE5 dataset has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5 spatial resolution but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower-resolution ERA-Interim data. Evaluation against meteorological observations at 13 globally distributed FLUXNET2015 sites shows that, on average, WFDE5 has lower mean absolute error and higher correlation than WFDEI for all variables. Bias-adjusted monthly precipitation totals of WFDE5 result in more plausible global hydrological water balance components when analysed in an uncalibrated hydrological model (WaterGAP) than with the use of raw ERA5 data for model forcing. The dataset, which can be downloaded from https://doi.org/10.24381/cds.20d54e34 (C3S, 2020b), is distributed by the Copernicus Climate Change Service (C3S) through its Climate Data Store (CDS, C3S, 2020a) and currently spans from the start of January 1979 to the end of 2018. The dataset has been produced using a number of CDS Toolbox applications, whose source code is available with the data - allowing users to regenerate part of the dataset or apply the same approach to other data. Future updates are expected spanning from 1950 to the most recent year. A sample of the complete dataset, which covers the whole of the year 2016, is accessible without registration to the CDS at https://doi.org/10.21957/935p-cj60 (Cucchi et al., 2020). © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License.