| 1. |
- Feldhoff, Jan H., et al.
(författare)
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Complex networks for climate model evaluation with application to statistical versus dynamical modeling of South American climate
- 2015
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Ingår i: Climate Dynamics. - : Springer Science and Business Media LLC. - 0930-7575 .- 1432-0894. ; 44:06-maj, s. 1567-1581
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Tidskriftsartikel (refereegranskat)abstract
- In this study we introduce two new node-weighted difference measures on complex networks as a tool for climate model evaluation. The approach facilitates the quantification of a model's ability to reproduce the spatial covariability structure of climatological time series. We apply our methodology to compare the performance of a statistical and a dynamical regional climate model simulating the South American climate, as represented by the variables 2 m temperature, precipitation, sea level pressure, and geopotential height field at 500 hPa. For each variable, networks are constructed from the model outputs and evaluated against a reference network, derived from the ERA-Interim reanalysis, which also drives the models. We compare two network characteristics, the (linear) adjacency structure and the (nonlinear) clustering structure, and relate our findings to conventional methods of model evaluation. To set a benchmark, we construct different types of random networks and compare them alongside the climate model networks. Our main findings are: (1) The linear network structure is better reproduced by the statistical model statistical analogue resampling scheme (STARS) in summer and winter for all variables except the geopotential height field, where the dynamical model CCLM prevails. (2) For the nonlinear comparison, the seasonal differences are more pronounced and CCLM performs almost as well as STARS in summer (except for sea level pressure), while STARS performs better in winter for all variables.
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| 2. |
- Frieler, K, et al.
(författare)
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Assessing the impacts of 1.5° C global warming - simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)
- 2017
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Ingår i: Geoscientific Model Development. - : Copernicus GmbH. - 1991-959X .- 1991-9603. ; 10, s. 4321-4345
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Tidskriftsartikel (refereegranskat)abstract
- In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Con- vention on Climate Change (UNFCCC) invited the Inter- governmental Panel on Climate Change (IPCC) to provide a “special report in 2018 on the impacts of global warming of 1.5 â—ŠC above pre-industrial levels and related global green- house gas emission pathwaysâ€. In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we de- scribe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is de- signed to allow for (1) separation of the impacts of histori- cal warming starting from pre-industrial conditions from im- pacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simula- tions); (2) quantification of the impacts of additional warm- ing up to 1.5 â—ŠC, including a potential overshoot and long- term impacts up to 2299, and comparison to higher lev- els of global mean temperature change (based on the low- emissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the cli- mate effects based on the same climate scenarios while ac- counting for simultaneous changes in socio-economic con- ditions following the middle-of-the-road Shared Socioeco- nomic Pathway (SSP2, Fricko et al., 2016) and in particu- lar differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0.With the aim of providing the scientific basis for an aggregation of impacts across sectors and anal- ysis of cross-sectoral interactions that may dampen or am- plify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact mod- els across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiver- sity).
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| 3. |
- Lange, Stefan, et al.
(författare)
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Local Difference Measures between Complex Networks for Dynamical System Model Evaluation
- 2015
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 10:4
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Tidskriftsartikel (refereegranskat)abstract
- A faithful modeling of real-world dynamical systems necessitates model evaluation. A recent promising methodological approach to this problem has been based on complex networks, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially explicit model evaluation. Building on a recent study by Feldhoff et al. [1] we comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system. Three types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchronizations of extreme rain events. An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required. Generalizations to directed as well as edge-and node-weighted graphs are discussed.
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| 4. |
- Lotze, Heike K., et al.
(författare)
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Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change
- 2019
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 116:26, s. 12907-12912
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Tidskriftsartikel (refereegranskat)abstract
- While the physical dimensions of climate change are now routinely assessed through multimodel intercomparisons, projected impacts on the global ocean ecosystem generally rely on individual models with a specific set of assumptions. To address these single-model limitations, we present standardized ensemble projections from six global marine ecosystem models forced with two Earth system models and four emission scenarios with and without fishing. We derive average biomass trends and associated uncertainties across the marine food web. Without fishing, mean global animal biomass decreased by 5% (+/- 4% SD) under low emissions and 17% (+/- 11% SD) under high emissions by 2100, with an average 5% decline for every 1 degrees C of warming. Projected biomass declines were primarily driven by increasing temperature and decreasing primary production, and were more pronounced at higher trophic levels, a process known as trophic amplification. Fishing did not substantially alter the effects of climate change. Considerable regional variation featured strong biomass increases at high latitudes and decreases at middle to low latitudes, with good model agreement on the direction of change but variable magnitude. Uncertainties due to variations in marine ecosystem and Earth system models were similar. Ensemble projections performed well compared with empirical data, emphasizing the benefits of multimodel inference to project future outcomes. Our results indicate that global ocean animal biomass consistently declines with climate change, and that these impacts are amplified at higher trophic levels. Next steps for model development include dynamic scenarios of fishing, cumulative human impacts, and the effects of management measures on future ocean biomass trends.
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| 5. |
- Schewe, Jacob, et al.
(författare)
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State-of-the-art global models underestimate impacts from climate extremes
- 2019
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Global impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.
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| 6. |
- Tittensor, Derek P., et al.
(författare)
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A protocol for the intercomparison of marine fishery and ecosystem models : Fish-MIP v1.0
- 2018
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Ingår i: Geoscientific Model Development. - : Copernicus GmbH. - 1991-959X .- 1991-9603. ; 11:4, s. 1421-1442
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Tidskriftsartikel (refereegranskat)abstract
- Model intercomparison studies in the climate and Earth sciences communities have been crucial to building credibility and coherence for future projections. They have quantified variability among models, spurred model development, contrasted within- and among-model uncertainty, assessed model fits to historical data, and provided ensemble projections of future change under specified scenarios. Given the speed and magnitude of anthropogenic change in the marine environment and the consequent effects on food security, biodiversity, marine industries, and society, the time is ripe for similar comparisons among models of fisheries and marine ecosystems. Here, we describe the Fisheries and Marine Ecosystem Model Intercomparison Project protocol version 1.0 (Fish-MIP v1.0), part of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which is a cross-sectoral network of climate impact modellers. Given the complexity of the marine ecosystem, this class of models has substantial heterogeneity of purpose, scope, theoretical underpinning, processes considered, parameterizations, resolution (grain size), and spatial extent. This heterogeneity reflects the lack of a unified understanding of the marine ecosystem and implies that the assemblage of all models is more likely to include a greater number of relevant processes than any single model. The current Fish-MIP protocol is designed to allow these heterogeneous models to be forced with common Earth System Model (ESM) Coupled Model Intercomparison Project Phase 5 (CMIP5) outputs under prescribed scenarios for historic (from the 1950s) and future (to 2100) time periods; it will be adapted to CMIP phase 6 (CMIP6) in future iterations. It also describes a standardized set of outputs for each participating Fish-MIP model to produce. This enables the broad characterization of differences between and uncertainties within models and projections when assessing climate and fisheries impacts on marine ecosystems and the services they provide. The systematic generation, collation, and comparison of results from Fish-MIP will inform an understanding of the range of plausible changes in marine ecosystems and improve our capacity to define and convey the strengths and weaknesses of model-based advice on future states of marine ecosystems and fisheries. Ultimately, Fish-MIP represents a step towards bringing together the marine ecosystem modelling community to produce consistent ensemble medium- and long-term projections of marine ecosystems.
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