ORCID Profile
0000-0003-3922-9833
Current Organisations
University of the Punjab, Lahore
,
Max Planck Institute for Meteorology
,
University of Colorado at Boulder
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Physical oceanography | Climate change processes | Atmospheric dynamics | Atmospheric Sciences | Climate change science | Atmospheric Dynamics | Physical Oceanography | Meteorology | Climate Change Processes
Atmospheric Processes and Dynamics | Climate Variability (excl. Social Impacts) | Climate Change Models |
Publisher: Copernicus GmbH
Date: 30-10-2020
Abstract: Abstract. Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool for quantifying the forced response and internal variability in various components of the climate system. However, there is no consensus on the ideal or even sufficient ensemble size for a large ensemble. Here, we introduce an objective method to estimate the required ensemble size that can be applied to any given application and demonstrate its use on the ex les of global mean near-surface air temperature, local temperature and precipitation, and variability in the El Niño–Southern Oscillation (ENSO) region and central United States for the Max Planck Institute Grand Ensemble (MPI-GE). Estimating the required ensemble size is relevant not only for designing or choosing a large ensemble but also for designing targeted sensitivity experiments with a model. Where possible, we base our estimate of the required ensemble size on the pre-industrial control simulation, which is available for every model. We show that more ensemble members are needed to quantify variability than the forced response, with the largest ensemble sizes needed to detect changes in internal variability itself. Finally, we highlight that the required ensemble size depends on both the acceptable error to the user and the studied quantity.
Publisher: Copernicus GmbH
Date: 14-04-2023
Abstract: Abstract. Future changes in the El Niño–Southern Oscillation (ENSO) are uncertain, both because future projections differ between climate models and because the large internal variability of ENSO clouds the diagnosis of forced changes in observations and in idual climate model simulations. By leveraging 14 single model initial-condition large ensembles (SMILEs), we robustly isolate the time-evolving response of ENSO sea surface temperature (SST) variability to anthropogenic forcing from internal variability in each SMILE. We find nonlinear changes in time in many models and considerable inter-model differences in projected changes in ENSO and the mean-state tropical Pacific zonal SST gradient. We demonstrate a linear relationship between the change in ENSO SST variability and the tropical Pacific zonal SST gradient, although forced changes in the tropical Pacific SST gradient often occur later in the 21st century than changes in ENSO SST variability, which can lead to departures from the linear relationship. Single-forcing SMILEs show a potential contribution of anthropogenic forcing (aerosols and greenhouse gases) to historical changes in ENSO SST variability, while the observed historical strengthening of the tropical Pacific SST gradient sits on the edge of the model spread for those models for which single-forcing SMILEs are available. Our results highlight the value of SMILEs for investigating time-dependent forced responses and inter-model differences in ENSO projections. The nonlinear changes in ENSO SST variability found in many models demonstrate the importance of characterizing this time-dependent behavior, as it implies that ENSO impacts may vary dramatically throughout the 21st century.
Publisher: Copernicus GmbH
Date: 29-05-2020
Abstract: Abstract. Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple single-model initial-condition large ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, the framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Coupled Model Intercomparison Project CMIP5 and CMIP6 archives. The original approach is shown to work well at global scales (potential method bias 20 %), while at local to regional scales such as British Isles temperature or Sahel precipitation, there is a notable potential method bias (up to 50 %), and more accurate partitioning of uncertainty is achieved through the use of SMILEs. Whenever internal variability and forced changes therein are important, the need to evaluate and improve the representation of variability in models is evident. The available SMILEs are shown to be a good representation of the CMIP5 model ersity in many situations, making them a useful tool for interpreting CMIP5. CMIP6 often shows larger absolute and relative model uncertainty than CMIP5, although part of this difference can be reconciled with the higher average transient climate response in CMIP6. This study demonstrates the added value of a collection of SMILEs for quantifying and diagnosing uncertainty in climate projections.
Publisher: Copernicus GmbH
Date: 02-12-2022
Publisher: Wiley
Date: 23-10-2020
Publisher: Springer Science and Business Media LLC
Date: 23-04-2015
DOI: 10.1038/NCLIMATE2575
Publisher: Springer Science and Business Media LLC
Date: 18-10-2023
Publisher: Frontiers Media SA
Date: 30-06-2015
Publisher: Copernicus GmbH
Date: 11-08-2022
Publisher: Copernicus GmbH
Date: 06-09-2022
Abstract: Abstract. The El Niño–Southern Oscillation (ENSO) occurs in three phases: neutral, warm (El Niño), and cool (La Niña). While classifying El Niño and La Niña is relatively straightforward, El Niño events can be broadly classified into two types: central Pacific (CP) and eastern Pacific (EP). Differentiating between CP and EP events is currently dependent on both the method and observational dataset used. In this study, we create a new classification scheme using supervised machine learning trained on 18 observational and re-analysis products. This builds on previous work by identifying classes of events using the temporal evolution of sea surface temperature in multiple regions across the tropical Pacific. By applying this new classifier to seven single model initial-condition large ensembles (SMILEs) we investigate both the internal variability and forced changes in each type of ENSO event, where events identified behave similarly to those observed. It is currently debated whether the observed increase in the frequency of CP events after the late 1970s is due to climate change. We found it to be within the range of internal variability in the SMILEs for trends after 1950, but not for the full observed period (1896 onwards). When considering future changes, we do not project a change in CP frequency or litude under a strong warming scenario (RCP8.5/SSP370) and we find model differences in EP El Niño and La Niña frequency and litude projections. Finally, we find that models show differences in projected precipitation and sea surface temperature (SST) pattern changes for each event type that do not seem to be linked to the Pacific mean state SST change, although the SST and precipitation changes in in idual SMILEs are linked. Our work demonstrates the value of combining machine learning with climate models, and highlights the need to use SMILEs when evaluating ENSO in climate models because of the large spread of results found within a single model due to internal variability alone.
Publisher: IOP Publishing
Date: 18-10-2022
Abstract: In this study, we investigate whether the Pacific decadal oscillation (PDO) can enhance or diminish El Niño Southern Oscillation (ENSO) temperature and precipitation teleconnections over North America using five single model initial-condition large ensembles (SMILEs). The use of SMILEs facilitates a statistically robust comparison of ENSO events that occur during different phases of the PDO. We find that a positive PDO enhances winter and spring El Niño temperature and precipitation teleconnections and diminishes La Niña teleconnections. A negative PDO has the opposite effect. The modulation of ENSO by the PDO is mediated by differences in the location and strength of the Aleutian Low and Pacific Jet during ENSO events under different phases of the PDO. This modulation is a simple combination of the in idual effects of the PDO and ENSO over North America. Finally, we show that ENSO and the PDO can be used to evaluate the likelihood of the occurrence of temperature and precipitation anomalies in different regions, but cannot be used as a deterministic predictor of these anomalies due to the large variability between in idual events.
Publisher: Springer Science and Business Media LLC
Date: 29-05-2021
DOI: 10.1007/S00382-021-05821-W
Abstract: We use a methodological framework exploiting the power of large ensembles to evaluate how well ten coupled climate models represent the internal variability and response to external forcings in observed historical surface temperatures. This evaluation framework allows us to directly attribute discrepancies between models and observations to biases in the simulated internal variability or forced response, without relying on assumptions to separate these signals in observations. The largest discrepancies result from the overestimated forced warming in some models during recent decades. In contrast, models do not systematically over- or underestimate internal variability in global mean temperature. On regional scales, all models misrepresent surface temperature variability over the Southern Ocean, while overestimating variability over land-surface areas, such as the Amazon and South Asia, and high-latitude oceans. Our evaluation shows that MPI-GE, followed by GFDL-ESM2M and CESM-LE offer the best global and regional representation of both the internal variability and forced response in observed historical temperatures.
Publisher: Copernicus GmbH
Date: 21-04-2023
Abstract: Abstract. Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictor variables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporal oscillations driven by internal climate variability can be a large source of statistical model error. Using pattern recognition techniques that exploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducing random errors in regional emulation tools. Here, we test two pattern recognition methods based on empirical orthogonal functions (EOFs), namely signal-to-noise maximizing EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean dynamic sea-level change. We use the Max Planck Institute Grand Ensemble (MPI-GE) as a test bed for both methods, as it is a type of initial-condition large ensemble designed for an optimal characterization of the externally forced response. We show that the two methods tested here more efficiently reduce errors than conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterizing their common response to external forcing reduces the random error by almost 60 %, a reduction that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single-realization modeling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern filtering leads to a varying degree of error reduction depending on the model and scenario, ranging from more than 20 % to about 70 % reduction in global-mean root mean squared error compared with unfiltered simulations. Our results highlight the relevance of pattern recognition methods as a tool to reduce errors in regional emulation tools of ocean dynamic sea-level change, especially when one or only a few realizations are available. Removing internal variability prior to tuning regional emulation tools can optimize the performance of the statistical model, leading to substantial differences in emulated dynamic sea level compared to unfiltered simulations.
Publisher: American Geophysical Union (AGU)
Date: 25-07-2015
DOI: 10.1002/2015GL064751
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-7816
Abstract: & & The El Ni& #241 o-Southern Oscillation (ENSO) is the leading mode of tropical climate variability, with impacts on ecosystems, agriculture, freshwater supplies, and hydropower production spanning much of the globe. Most impact studies use a canonical representation of ENSO, as characterised by sea-surface temperature anomalies (SSTa) in the central-eastern Pacific. However, ENSO shows large differences from one event to another in terms of its intensity, spatial pattern and temporal evolution. For instance, while the 1997/98 El Ni& #241 o displayed extreme SSTa in the eastern equatorial Pacific, the largest SSTa during the 2002/03 event were weaker and primarily confined to the central equatorial Pacific. These differences in the longitudinal location and intensity of ENSO events, referred to as & #8220 ENSO ersity& #8221 , are associated with different regional climate impacts throughout the world. The representation of such differences in ENSO spatial patterns in climate models thus strongly influence the skill of impact prediction systems. Here, we exploit the power of single model initial-condition large ensembles (SMILEs) from 14 fully-coupled climate models from both CMIP5 and CMIP6 (totalling over 500 simulations in historical and SSP-RCP scenarios) to examine the system trajectories, and identify future variations in the location and intensity of El Ni& #241 o and La Ni& #241 a events. We then quantify how contrasting pathways for ENSO event location, and their associated intensity, could alter seasonal precipitation anomalies throughout the world over the 21& sup& st& /sup& century.& &
Publisher: Copernicus GmbH
Date: 05-02-2020
Publisher: Springer Science and Business Media LLC
Date: 04-02-2021
DOI: 10.1038/S41467-020-20635-W
Abstract: Separating how model-to-model differences in the forced response ( U MD ) and internal variability ( U IV ) contribute to the uncertainty in climate projections is important, but challenging. Reducing U MD increases confidence in projections, while U IV characterises the range of possible futures that might occur purely by chance. Separating these uncertainties is limited in traditional multi-model ensembles because most models have only a small number of realisations furthermore, some models are not independent. Here, we use six largely independent single model initial-condition large ensembles to separate the contributions of U MD and U IV in projecting 21st-century changes of temperature, precipitation, and their temporal variability under strong forcing (RCP8.5). We provide a method that produces similar results using traditional multi-model archives. While U MD is larger than U IV for both temperature and precipitation changes, U IV is larger than U MD for the changes in temporal variability of both temperature and precipitation, between 20° and 80° latitude in both hemispheres. Over large regions and for all variables considered here except temporal temperature variability, models agree on the sign of the forced response whereas they disagree widely on the magnitude. Our separation method can readily be extended to other climate variables.
Publisher: American Geophysical Union (AGU)
Date: 27-10-2018
DOI: 10.1029/2018GL079764
Abstract: Two large ensembles are used to quantify the extent to which internal variability can contribute to long‐term changes in El Niño‐Southern Oscillation (ENSO) characteristics. We diagnose changes that are externally forced and distinguish between multi‐model simulation results that differ by chance and those that differ due to different model physics. The range of simulated ENSO litude changes in the large ensemble historical simulations encompasses 90% of the Coupled Model Intercomparison Project 5 historical simulations and 80% of moderate (RCP4.5) and strong (RCP8.5) warming scenarios. When considering projected ENSO pattern changes, model differences are also important. We find that ENSO has high internal variability and that single realizations of a model can produce very different results to the ensemble mean response. Due to this variability, 30–40 ensemble members of a single model are needed to robustly compute absolute ENSO variance to a 10% error when 30‐year analysis periods are used.
Publisher: Journal of Marine Research/Yale
Date: 07-2011
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-7791
Abstract: El Ni& #241 o Southern Oscillation (ENSO) shows large differences from one event to another in terms of its intensity, spatial pattern, and temporal evolution, which are typically referred to as & #8220 ENSO ersity& #8221 . While such differences in ENSO patterns are associated with different regional climate impacts throughout the world, influencing the skill of impact prediction systems, large uncertainties remain concerning its potential future evolution and trends. The location and intensity of ENSO events are indeed strongly influenced by internal/natural climate variations, limiting the detection of forced changes.Here, we exploit the power of single model initial-condition large ensembles (SMILEs) from 13 fully coupled climate models from both CMIP5 and CMIP6 (totalling 580 realizations in historical and SSP-RCP scenarios) to first examine the ability of climate models to simulate realistic ersity of ENSO events compared to multiple observational datasets, and then use those models to characterize future trajectories in the location and intensity of El Ni& #241 o and La Ni& #241 a events. We define the location of ENSO events as the longitude of the absolute maximum (the intensity) of sea-surface temperature anomalies (SSTa) during boreal Winter (December-February) in the equatorial Pacific. Future projections of ENSO ersity are assessed in terms of joint probability distributions of ENSO events& #8217 location and intensity.While some models show a degree of ersity in the location and intensity of events that are comparable with observed statistics, other models tend to favour the occurrence of eastern or central Pacific events. Such contrasting performances during the historical period are found to be associated with different future trajectories of ENSO ersity: i) models favouring the occurrence of eastern Pacific events (e.g., ACCESS-ESM1-5, CanESM2, and 5) show a westward shift in event location over the 21st century ii) models simulating ENSO events anomalously westward tend to show an eastward shift in event locations and an increased intensity in the 21st century (e.g., CESM1 and 2, CSIRO-MK3-6-0, GFDL-CM3, GFDL-ESM2M, MIROC-ES2L, MIROC6). Nevertheless, we note that models showing the closest match to observed statistics during the historical period also present a westward shift in ENSO locations and a slight increase in intensity in the 21st century (e.g., GFDL-SPEAR and IPSL-CM6-LR).Although the physical cause of model discrepancies remains unclear, this study provides a broader perspective on expected ENSO changes over the 21st century in different models and highlights the spread of projections among models.
Publisher: Springer Science and Business Media LLC
Date: 16-10-2017
Publisher: Copernicus GmbH
Date: 22-04-2021
Abstract: Abstract. Single model initial-condition large ensembles (SMILEs) are valuable tools that can be used to investigate the climate system. SMILEs allow scientists to quantify and separate the internal variability of the climate system and its response to external forcing, with different types of SMILEs appropriate to answer different scientific questions. In this editorial we first provide an introduction to SMILEs and an overview of the studies in the special issue “Large Ensemble Climate Model Simulations: Exploring Natural Variability, Change Signals and Impacts”. These studies analyse a range of different types of SMILEs including global climate models (GCMs), regionally downscaled climate models (RCMs), a hydrological model with input from a RCM SMILE, a SMILE with prescribed sea surface temperature (SST) built for event attribution, a SMILE that assimilates observed data, and an initialised regional model. These studies provide novel methods, that can be used with SMILEs. The methods published in this issue include a snapshot empirical orthogonal function analysis used to investigate El Niño–Southern Oscillation teleconnections the partitioning of future uncertainty into model differences, internal variability, and scenario choices a weighting scheme for multi-model ensembles that can incorporate SMILEs and a method to identify the required ensemble size for any given problem. Studies in this special issue also focus on RCM SMILEs, with projections of the North Atlantic Oscillation and its regional impacts assessed over Europe, and an RCM SMILE intercomparison. Finally a subset of studies investigate projected impacts of global warming, with increased water flows projected for future hydrometeorological events in southern Ontario precipitation projections over central Europe are investigated and found to be inconsistent across models in the Alps, with a continuation of past tendencies in Mid-Europe and equatorial Asia is found to have an increase in the probability of large fire and drought events under higher levels of warming. These studies demonstrate the utility of different types of SMILEs. In the second part of this editorial we provide a perspective on how three types of SMILEs could be combined to exploit the advantages of each. To do so we use a GCM SMILE and an RCM SMILE with all forcings, as well as a naturally forced GCM SMILE (nat-GCM) over the European domain. We utilise one of the key advantages of SMILEs, precisely separating the forced response and internal variability within an in idual model to investigate a variety of simple questions. Broadly we show that the GCM can be used to investigate broad-scale patterns and can be directly compared to the nat-GCM to attribute forced changes to either anthropogenic emissions or volcanoes. The RCM provides high-resolution spatial information of both the forced change and the internal variability around this change at different warming levels. By combining all three ensembles we can gain information that would not be available using a single type of SMILE alone, providing a perspective on future research that could be undertaken using these tools.
Publisher: Naturalis Biodiversity Center
Date: 31-12-2019
DOI: 10.3767/PERSOONIA.2019.43.06
Abstract: Novel species of fungi described in this study include those from various countries as follows: Antarctica , Apenidiella antarctica from permafrost, Cladosporium fildesense fromanunidentifiedmarinesponge. Argentina , Geastrum wrightii onhumusinmixedforest. Australia , Golovinomyces glandulariae on Glandularia aristigera, Neoanungitea eucalyptorum on leaves of Eucalyptus grandis, Teratosphaeria corymbiicola on leaves of Corymbia ficifolia, Xylaria eucalypti on leaves of Eucalyptus radiata. Brazil, Bovista psammophila on soil, Fusarium awaxy on rotten stalks of Zea mays, Geastrum lanuginosum on leaf litter covered soil, Hermetothecium mikaniae-micranthae (incl. Hermetothecium gen. nov.)on Mikania micrantha, Penicillium reconvexovelosoi in soil, Stagonosporopsis vannaccii from pod of Glycine max. British Virgin Isles , Lactifluus guanensis onsoil. Canada , Sorocybe oblongispora on resin of Picea rubens. Chile, Colletotrichum roseum on leaves of Lapageria rosea. China, Setophoma caverna fromcarbonatiteinKarstcave. Colombia , Lareunionomyces eucalypticola on leaves of Eucalyptus grandis. Costa Rica, Psathyrella pivae onwood. Cyprus , Clavulina iris oncalcareoussubstrate. France , Chromosera ambigua and Clavulina iris var. occidentalis onsoil. French West Indies , Helminthosphaeria hispidissima ondeadwood. Guatemala , Talaromyces guatemalensis insoil. Malaysia , Neotracylla pini (incl. Tracyllales ord. nov. and Neotra- cylla gen. nov.)and Vermiculariopsiella pini on needles of Pinus tecunumanii. New Zealand, Neoconiothyrium viticola on stems of Vitis vinifera, Parafenestella pittospori on Pittosporum tenuifolium, Pilidium novae-zelandiae on Phoenix sp. Pakistan , Russula quercus-floribundae onforestfloor. Portugal , Trichoderma aestuarinum from salinewater. Russia , Pluteus liliputianus on fallen branch of deciduous tree, Pluteus spurius on decaying deciduouswoodorsoil. South Africa , Alloconiothyrium encephalarti, Phyllosticta encephalarticola and Neothyrostroma encephalarti (incl. Neothyrostroma gen. nov.)onleavesof Encephalartos sp., Chalara eucalypticola on leaf spots of Eucalyptus grandis × urophylla, Clypeosphaeria oleae on leaves of Olea capensis, Cylindrocladiella postalofficium on leaf litter of Sideroxylon inerme , Cylindromonium eugeniicola (incl. Cylindromonium gen. nov.)onleaflitterof Eugenia capensis , Cyphellophora goniomatis on leaves of Gonioma kamassi , Nothodactylaria nephrolepidis (incl. Nothodactylaria gen. nov. and Nothodactylariaceae fam. nov.)onleavesof Nephrolepis exaltata , Falcocladium eucalypti and Gyrothrix eucalypti on leaves of Eucalyptus sp., Gyrothrix oleae on leaves of Olea capensis subsp. macrocarpa , Harzia metro sideri on leaf litter of Metrosideros sp., Hippopotamyces phragmitis (incl. Hippopota- myces gen. nov.)onleavesof Phragmites australis , Lectera philenopterae on Philenoptera violacea , Leptosillia mayteni on leaves of Maytenus heterophylla , Lithohypha aloicola and Neoplatysporoides aloes on leaves of Aloe sp., Millesimomyces rhoicissi (incl. Millesimomyces gen. nov.) on leaves of Rhoicissus digitata , Neodevriesia strelitziicola on leaf litter of Strelitzia nicolai , Neokirramyces syzygii (incl. Neokirramyces gen. nov.)onleafspotsof
Publisher: American Geophysical Union (AGU)
Date: 20-08-2010
DOI: 10.1002/2014GL060527
Publisher: Springer Science and Business Media LLC
Date: 07-03-2016
DOI: 10.1038/NCLIMATE2941
Publisher: Springer Science and Business Media LLC
Date: 18-05-2018
Publisher: Copernicus GmbH
Date: 11-08-2022
DOI: 10.5194/ESD-2022-26
Abstract: Abstract. Future changes in the El Niño–Southern Oscillation (ENSO) are uncertain, both because future projections differ between climate models and because the large internal variability of ENSO clouds the diagnosis of forced changes in observations and in idual climate model simulations. By leveraging 14 single model initial-condition large ensembles (SMILEs), we robustly isolate the time evolving response of ENSO sea surface temperature (SST) variability to anthropogenic forcing from internal variability in each SMILE. We find non-linear changes in time in many models and considerable inter-model differences in projected changes in ENSO and the mean-state tropical Pacific zonal SST gradient. We demonstrate a linear relationship between the change in ENSO SST variability and the tropical Pacific zonal SST gradient although forced changes in the tropical Pacific SST gradient often occur later in the 21st century than changes in ENSO SST variability, which can lead to departures from the linear relationship. Single forcing SMILEs show a potential contribution of anthropogenic forcing (aerosols and greenhouse gases) to historical changes in ENSO SST variability, while the observed historical strengthening of the tropical Pacific SST gradient sits on the edge of the model spread for those models for which single forcing SMILEs are available. Our results highlight the value of SMILEs for investigating time-dependent forced responses and inter-model differences in ENSO projections. The non-linear changes in ENSO SST variability found in many models demonstrate the importance of characterising this time-dependent behaviour, as it implies that ENSO impacts may vary dramatically throughout the 21st century.
Publisher: Springer Science and Business Media LLC
Date: 04-06-2016
DOI: 10.1007/S10661-016-5380-6
Abstract: The use of the marine gastropod, Cellana tramoserica, as a biomonitor of metal exposure was investigated. The factors influencing metal concentrations, such as mass, gender, substrate, shoreline position and temporal variation were examined. Tissue metal concentrations were mostly found to be independent of mass and gender. When metal concentrations were significantly correlated with mass, correlations were low and explained little variability. The underlying substrate and position in the littoral zone had only a small influence on metal concentrations. Variation between in iduals, inherent variability due to genetic variability, was the most significant contribution to the overall variation in metal concentrations, resulting in positive skewing of population distributions. The mean metal concentrations varied temporally metal masses were relatively constant with fluctuations in metal concentrations related to fluctuations in metal body burdens. The populations from a metal-contaminated site had significantly higher tissue Cu, Zn, As and Pb concentrations than the populations from relatively uncontaminated locations. C. tramoserica therefore can be considered to be a net accumulator of metals. A s le number of >10 is required to detect changes of 25 % from the mean concentrations at uncontaminated locations. This species meets the requirements of a suitable biomonitor for metal contaminants in the environment i.e. hardy, sessile, widespread, sufficient tissue mass and a metal accumulator. As the measurement of metal concentrations in C. tramesoria were influenced by substrate and shore position and, sometimes, mass, sites with similar substrates and organisms of similar mass and shoreline position should be chosen for comparison. When comparing metal concentrations in gastropods from different locations, they should be collected over the same period to minimise variability due to mass differences, spawning and other seasonal/temporal effects.
Publisher: American Geophysical Union (AGU)
Date: 19-12-2015
DOI: 10.1002/2015GL066608
Publisher: Copernicus GmbH
Date: 05-02-2020
DOI: 10.5194/ESD-2019-93
Abstract: Abstract. Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty, and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple Single-Model Initial-Condition Large Ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, the iconic framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Climate Model Intercomparison Projects CMIP5 and CMIP6 archives. The original approach is shown to work well at global scales (potential method error
Publisher: Copernicus GmbH
Date: 29-11-2019
DOI: 10.5194/ESD-2019-70
Abstract: Abstract. Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool to quantify the forced response and internal variability in various components of the climate system. However, there is no consensus on the ideal or even sufficient ensemble size for a large ensemble. Here, we introduce an objective method to estimate the required ensemble size that can be applied to any given application and demonstrate its use on the ex les of global mean surface temperature, local surface temperature and precipitation and variability in the ENSO region and central America. Where possible, we base our estimate of the required ensemble size on the pre-industrial control simulation, which is available for every model. First, we determine how much of an available ensemble size is interpretable without a substantial impact of res ling ensemble members. Then, we show that more ensemble members are needed to quantify variability than the forced response, with the largest ensemble sizes needed to detect changes in internal variability itself. Finally, we highlight that the required ensemble size depends on both the acceptable error to the user and the studied quantity.
Publisher: Springer Science and Business Media LLC
Date: 12-10-2019
Publisher: Copernicus GmbH
Date: 19-08-2020
DOI: 10.5194/ESD-2020-63
Abstract: Abstract. Using the Max Planck Institute Grand Ensemble (MPI-GE) with 200 members for the historical simulation (1850–2005), we investigate the impact of the spatial distribution of volcanic aerosols on the ENSO response. In particular, we select 3 eruptions (El Chichón, Agung and Pinatubo) in which the aerosol is respectively confined to the Northern Hemisphere, the Southern Hemisphere or equally distributed across the equator. Our results show that the ENSO anomalies start at the end of the year of the eruption and peak the following one. Especially, we found that when the aerosol is located in the Northern Hemisphere or is symmetrically distributed, El Niño-like anomalies develop while aerosol distribution confined to the Southern Hemisphere leads to a La Niña-like anomaly. Our results strongly point to the volcanically induced displacement of the ITCZ as the main mechanism that drives the ENSO response, while suggesting that the other mechanisms (the ocean dynamical thermostat, the cooling of tropical northern Africa or of the Maritime continent) commonly invoked to explain the post-eruption ENSO response appear not to be at play in our model.
Publisher: American Meteorological Society
Date: 20-08-2020
Abstract: The representation of tropical precipitation is evaluated across three generations of models participating in phases 3, 5, and 6 of the Coupled Model Intercomparison Project (CMIP). Compared to state-of-the-art observations, improvements in tropical precipitation in the CMIP6 models are identified for some metrics, but we find no general improvement in tropical precipitation on different temporal and spatial scales. Our results indicate overall little changes across the CMIP phases for the summer monsoons, the double-ITCZ bias, and the diurnal cycle of tropical precipitation. We find a reduced amount of drizzle events in CMIP6, but tropical precipitation occurs still too frequently. Continuous improvements across the CMIP phases are identified for the number of consecutive dry days, for the representation of modes of variability, namely, the Madden–Julian oscillation and El Niño–Southern Oscillation, and for the trends in dry months in the twentieth century. The observed positive trend in extreme wet months is, however, not captured by any of the CMIP phases, which simulate negative trends for extremely wet months in the twentieth century. The regional biases are larger than a climate change signal one hopes to use the models to identify. Given the pace of climate change as compared to the pace of model improvements to simulate tropical precipitation, we question the past strategy of the development of the present class of global climate models as the mainstay of the scientific response to climate change. We suggest the exploration of alternative approaches such as high-resolution storm-resolving models that can offer better prospects to inform us about how tropical precipitation might change with anthropogenic warming.
Publisher: Copernicus GmbH
Date: 02-12-2022
DOI: 10.5194/EGUSPHERE-2022-1293
Abstract: Abstract. Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictor variables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporal oscillations driven by internal climate variability can be a large source of model disagreement. Using pattern recognition techniques that exploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducing random errors in regional emulation tools. Here, we test two pattern recognition methods based on Empirical Orthogonal Functions (EOF), namely signal-to-noise maximising EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean dynamic sea-level change. These two methods are applied to the initial-condition large ensemble MPI-GE, so that internal variability is optimally characterized while avoiding model biases. We show that pattern filtering provides an efficient way of reducing errors compared to other conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterising their common response to external forcing reduces the random error by almost 60 %, a reduction level that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single realization modelling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern scaling leads to a varying degree of error reduction depending on the model and scenario, ranging from more than 20 % to about 70 % reduction in global-mean root-mean-squared error compared with unfiltered simulations. Our results highlight the relevance of pattern recognition methods as a tool to reduce errors in regional emulation tools of ocean dynamic sea-level change, especially when one or a few realizations are available. Removing internal variability prior to tuning regional emulation tools can optimize the performance of the statistical model and simplify the choice of suitable predictors.
Publisher: Copernicus GmbH
Date: 17-09-2021
Abstract: Abstract. Using the Max Planck Institute Grand Ensemble (MPI-GE) with 200 members for the historical simulation (1850–2005), we investigate the impact of the spatial distribution of volcanic aerosols on the El Niño–Southern Oscillation (ENSO) response. In particular, we select three eruptions (El Chichón, Agung and Pinatubo) in which the aerosol is respectively confined to the Northern Hemisphere, the Southern Hemisphere or equally distributed across the Equator. Our results show that relative ENSO anomalies start at the end of the year of the eruption and peak in the following one. We especially found that when the aerosol is located in the Northern Hemisphere or is symmetrically distributed, relative El Niño-like anomalies develop, while aerosol distribution confined to the Southern Hemisphere leads to a relative La Niña-like anomaly. Our results point to the volcanically induced displacement of the Intertropical Convergence Zone (ITCZ) as a key mechanism that drives the ENSO response, while suggesting that the other mechanisms (the ocean dynamical thermostat and the cooling of tropical northern Africa or the Maritime Continent) commonly invoked to explain the post-eruption ENSO response may be less important in our model.
Publisher: American Geophysical Union (AGU)
Date: 07-2019
DOI: 10.1029/2019MS001639
Publisher: IEEE
Date: 04-2021
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-6990
Abstract: Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictor variables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporal oscillations driven by internal climate variability can be a large source of model disagreement. Using pattern recognition techniques that exploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducing random errors in regional emulation tools. Here, we test two pattern recognition methods based on Empirical Orthogonal Functions (EOF), namely signal-to-noise maximising EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean dynamic sea-level change. These two methods are applied to an initial-condition large ensemble (MPI-GE), so that its externally forced signal is optimally characterized. We show that pattern filtering provides an efficient way of reducing errors compared to other conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterising their common response to external forcing reduces the random error by almost 60%, a reduction level that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single realization modelling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern scaling leads to a varying degree of error reduction depending on the model and scenario, ranging from more than 20% to about 70% reduction in global-mean mean-squared error compared with unfiltered simulations. Our results highlight the relevance of pattern recognition methods as a means of reducing errors in regional emulation tools of ocean dynamic sea-level change, especially when one or a few realizations are available.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-10531
Abstract: Projecting how temperature variability is likely to change in the future is important for understanding future extreme events. This comes from the fact that such extremes can change due to both changes in the mean climate and its variability. The recent IPCC report found large regions of low model agreement in the change of temperature variability in both December, January, February (DJF) and June, July, August (JJA) when considering 7 Single Model Initial-Condition Large Ensembles (SMILEs). In this study we use the framework described by Suarez-Gutierrez et al, (2021) to constrain future projections of temperature variability by selecting the SMILEs that best represent observed variability. We use 11 SMILEs with CMIP5 and CMIP6 forcing and consider 9 ocean regions and 24 land regions. We then assess, for both DJF and JJA, whether temperature variability projections are constrained by selecting for models capture observed variability in in idual regions and seasons. We consider projected changes at various warming levels to account for differences in warming between models and the use of different future scenarios across CMIP5 and 6. We identify MPI-GE and CESM2 as the SMILEs that capture observed variability sufficiently. across most regions (29 & 30 out of 33 in DJF and 28 and 26 in JJA respectively). Whether temperature variability projections are constrained depends on both season and region. For ex le, in DJF over South East Asia the constraint does not change the already large spread of projections. Conversely, over the Amazon the constraint tells us temperature variability will increase in DJF whereas the entire model archive does not agree on the sign of the change. This method can be used to better constrain our uncertainty in temperature variability projections by selecting SMILEs that best represent observed variability.
Start Date: 06-2023
End Date: 05-2026
Amount: $450,042.00
Funder: Australian Research Council
View Funded ActivityStart Date: 08-2017
End Date: 12-2024
Amount: $30,050,000.00
Funder: Australian Research Council
View Funded Activity