scholarly journals Challenges and opportunities to reduce uncertainty in projections of future atmospheric CO<sub>2</sub>: a combined marine and terrestrial biosphere perspective

2014 ◽  
Vol 11 (2) ◽  
pp. 2083-2153 ◽  
Author(s):  
D. Dalmonech ◽  
A. M. Foley ◽  
A. Anav ◽  
P. Friedlingstein ◽  
A. D. Friend ◽  
...  
Keyword(s):  
Climate Change ◽  
Performance Metrics ◽  
Model Performance ◽  
Atmospheric Co2 ◽  
Climate Projections ◽  
Future Directions ◽  
Challenges And Opportunities ◽  
The One ◽  
Earth System Models ◽  
Made In

Abstract. Atmospheric CO2 and climate projections for the next century vary widely across current Earth system models (ESMs), owing to different representations of the interactions between the marine and land carbon cycle on the one hand, and climate change and increasing atmospheric CO2 on the other hand. Several efforts have been made in the last years to analyse these differences in detail in order to suggest model improvements. Here we review these efforts and analyse their successes, but also the associated uncertainties that hamper the best use of the available observations to constrain and improve the ESMs models. The aim of this paper is to highlight challenges in improving the ESMs that result from: (i) uncertainty about important processes in terrestrial and marine ecosystems and their response to climate change and increasing atmospheric CO2; (ii) structural and parameter-related uncertainties in current land and marine models; (iii) uncertainties related to observations and the formulations of model performance metrics. We discuss the implications of these uncertainties for reducing the spread in future projections of ESMs and suggest future directions of work to overcome these uncertainties.

scholarly journals Climate change through the farming systems lens: challenges and opportunities for farming in Australia

2012 ◽  
Vol 63 (3) ◽  
pp. 203 ◽  
Author(s):  
Peter Hayman ◽  
Lauren Rickards ◽  
Richard Eckard ◽  
Deirdre Lemerle
Keyword(s):  
Climate Change ◽  
Farming Systems ◽  
Farming System ◽  
Rural Sociology ◽  
System Level ◽  
Climate Projections ◽  
Adaptation To Climate Change ◽  
Challenges And Opportunities ◽  
Emerging Issues ◽  
Reduce Emissions

Adaptation to and mitigation of climate change in Australian agriculture has included research at the plant, animal, and soil level; the farming system level; and the community and landscape level. This paper focuses on the farming systems level at which many of the impacts of a changing climate will be felt. This is also the level where much of the activity relating to adaptation and mitigation can usefully be analysed and at which existing adaptive capacity provides a critical platform for further efforts. In this paper, we use a framework of nested hierarchies introduced by J. Passioura four decades ago to highlight the need for research, development and extension (RDE) on climate change at the farming systems level to build on more fundamental soil, plant, and animal sciences and to link into higher themes of rural sociology and landscape science. The many questions asked by those managing farming systems can be categorised under four broad headings: (1) climate projections at a local scale, (2) impacts of climate projections on existing farming systems, (3) adaptation options, and (4) risks and opportunities from policies to reduce emissions. These questions are used as a framework to identify emerging issues for RDE in Australian farming systems, including the complex balance in on-farm strategies between adapting to climate change and reducing greenhouse gas concentrations. Climate is recognised as one of the defining features of different farming systems in Australia. It follows that if the climate changes, farming systems will have to shift, adapt, or be transformed into a different land use. Given that Australian farming systems have been adaptive in the past, we address the question of the extent to which research on adaptation to climate change in farming systems is different or additional to research on farming systems in a variable climate.

scholarly journals Estimating the potential impact of climate change on sunflower yield in the Konya province of Turkey

2021 ◽  
pp. 1-13
Author(s):  
Hudaverdi Gurkan ◽  
Vakhtang Shelia ◽  
Nilgun Bayraktar ◽  
Y. Ersoy Yildirim ◽  
Nebi Yesilekin ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Model Performance ◽  
Global Climate Models ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
Adaptation To Climate Change ◽  
Impact Of Climate Change ◽  
The Impact

Abstract The impact of climate change on agricultural productivity is difficult to assess. However, determining the possible effects of climate change is an absolute necessity for planning by decision-makers. The aim of the study was the evaluation of the CSM-CROPGRO-Sunflower model of DSSAT4.7 and the assessment of impact of climate change on sunflower yield under future climate projections. For this purpose, a 2-year sunflower field experiment was conducted under semi-arid conditions in the Konya province of Turkey. Rainfed and irrigated treatments were used for model analysis. For the assessment of impact of climate change, three global climate models and two representative concentration pathways, i.e. 4.5 and 8.5 were selected. The evaluation of the model showed that the model was able to simulate yield reasonably well, with normalized root mean square error of 1.3% for the irrigated treatment and 17.7% for the rainfed treatment, a d-index of 0.98 and a modelling efficiency of 0.93 for the overall model performance. For the climate change scenarios, the model predicted that yield will decrease in a range of 2.9–39.6% under rainfed conditions and will increase in a range of 7.4–38.5% under irrigated conditions. Results suggest that temperature increases due to climate change will cause a shortening of plant growth cycles. Projection results also confirmed that increasing temperatures due to climate change will cause an increase in sunflower water requirements in the future. Thus, the results reveal the necessity to apply adequate water management strategies for adaptation to climate change for sunflower production.

scholarly journals Vineyard-specific climate projections help growers manage risk and plan adaptation in the Paso Robles AVA

2022 ◽  
Vol 75 (3) ◽  
pp. 142-150
Author(s):  
Nicholas Babin ◽  
Jazlyn Guerrero ◽  
Diego Rivera ◽  
Ajay Singh
Keyword(s):  
Climate Change ◽  
Climate Adaptation ◽  
Climate Projections ◽  
Wine Grape ◽  
Support Tools ◽  
Plan Adaptation ◽  
The One ◽  
Dry Farming ◽  
Near Future

California's wine grape growers will face increasing challenges under a changing climate as most production occurs near the boundaries of current varieties' climatic thresholds. As part of this study, we developed a method for transforming downscaled climate information from the publicly available Cal-Adapt database into useful and useable climate projections for vineyard managers and advisors in the Paso Robles American Viticultural Area. We shared vineyard-specific projections during interviews of 20 managers and advisors. Overall, interviewees expressed trust in the projections and found them helpful in reducing their psychological distance from climate change. The projections prompted consideration of strategies for managing future climate risk and planning adaptation, with the majority of adaptations associated with long-term decisions such as row orientation, variety selection, dry farming, crop diversification and relocation. Agri-climatic decision support tools such as the one prototyped here may prove especially helpful for incorporating climate adaptation into the long-term business planning and vineyard redevelopment decisions facing managers and advisors in the near future. This approach could be extended to other California wine grape regions or to other perennial crops with expected vulnerabilities to climate change.

scholarly journals Assessing water supply capacity in a complex river basin under climate change using the logistic eco-engineering decision scaling framework

2018 ◽  
Author(s):  
Daeha Kim ◽  
Jong Ahn Chun ◽  
Si-Jung Choi
Keyword(s):  
Climate Change ◽  
Water Supply ◽  
River Basin ◽  
Performance Metrics ◽  
Large River ◽  
Climate Projections ◽  
Stress Tests ◽  
System Failures ◽  
Logistic Regressions ◽  
The Impact

Abstract. Climate change is a global stressor that can undermine water management policies developed under the assumption of stationary climate, necessitating robust solutions to reducing the risk of system failures for uncertain future climates. While the response-surface-based assessments have provided convenience to explore responsive behaviours of expected system performance to climatic stresses, they were unable to predict the risk of system failures from individual climate projections. In this study, we proposed to use the logistic regressions for evaluating the probability of non-successive outcomes against pre-defined thresholds directly from climate projections, which may be more informative for decision making processes than the expected performances. As a case study, water supply and ecological reliabilities within a large river basin were assessed by combining the eco-engineering decision scaling framework and the logistic regressions. The impact assessment for the South Korean river basin showed that optimal water supply performance at the sub-basins were expected to be satisfactory for the upcoming 20 years of 2020–2039, while the human-demand-only operations could lower the ecological reliabilities. When considering ecological demands in water operations to reduce the ecological vulnerabilities, the stakeholders should accept increasing risks of unsatisfactory supply at the sub-basins with low demands. This study highlights that binary conversions of the performance metrics from the stress tests allow users to measure the risks of system failures varying across sub-components and standpoints with minimal computational costs.

scholarly journals Hydrological model optimization using multi-gauge calibration (MGC) in a mountainous region

2020 ◽  
Author(s):  
Sead Ahmed Swalih ◽  
Ercan Kahya
Keyword(s):  
Climate Change ◽  
Model Calibration ◽  
Hydrological Model ◽  
Model Performance ◽  
Spatial Dimension ◽  
Model Parameters ◽  
Climate Projections ◽  
Predictive Capacity ◽  
Black Sea Region ◽  
The Impact

Abstract It is a challenge for hydrological models to capture complex processes in a basin with limited data when estimating model parameters. This study aims to contribute in this field by assessing the impact of incorporating spatial dimension on the improvement of model calibration. Hence, the main objective of this study was to evaluate the impact of multi-gauge calibration in hydrological model calibration for Ikizdere basin, Black Sea Region in Turkey. In addition, we have incorporated the climate change impact assessment for the study area. Four scenarios were tested for performance assessment of calibration: (1) using downstream flow data (DC), (2) using upstream data (UC), (3) using upstream and downstream data (Multi-Gauge Calibration – MGC), and (4) using upstream and then downstream data (UCDC). The results have shown that using individual gauges for calibration (1 and 2) improve the local predictive capacity of the model. MGC calibration significantly improved the model performance for the whole basin unlike 1 and 2. However, the local gauge calibrations statistical performance, compared to MGC outputs, was better for local areas. The UCDC yields the best model performance and much improved predictive capacity. Regarding the climate change, we did not observe an agreement amongst the future climate projections for the basin towards the end of the century.

scholarly journals Use of Genetic Algorithms for Ocean Model Parameter Optimisation

2021 ◽  
Author(s):  
Marcus Falls ◽  
Raffaele Bernardello ◽  
Miguel Castrillo ◽  
Mario Acosta ◽  
Joan Llort ◽  
...  
Keyword(s):  
Dimensional Space ◽  
Model Performance ◽  
Ocean Model ◽  
Global Ocean ◽  
Test Case ◽  
Additional Parameter ◽  
Estimation Of Parameters ◽  
The One ◽  
Optimal Set ◽  
Earth System Models

Abstract. When working with Earth system models, a considerable challenge that arises is the need to establish the set ofparameter values that ensure the optimal model performance in terms of how they reflect real-world observed data. Giventhat each additional parameter under investigation increases the dimensional space of the problem by one, simple brute-forcesensitivity tests can quickly become too computationally strenuous. In addition, the complexity of the model and interactionsbetween parameters mean that testing them on an individual basis has the potential to miss key information. As such, this5work argues the need of the development of a tool that can give an estimation of parameters. Specifically it proposes the useof a Biased Random Key Genetic Algorithm (BRKGA). This method is tested using the one dimensional configuration ofPISCES-v2, the biogeochemical component of NEMO, a global ocean model. A test case of particulate organic carbon in theNorth Atlantic down to 1000 m depth is examined, using observed data obtained from autonomous biogeochemical Argo floats.In this case, two sets of tests are run, one where each of the model outputs are compared to the model outputs with default10settings, and another where they are compared with 3 sets of observed data from their respective regions, which is followed bya cross reference of the results. The results of these analyses provide evidence that this approach is robust and consistent, andalso that it provides indication of the sensitivity of parameters on variables of interest. Given the deviation of the optimal set ofparameters from the default, further analyses using observed data in other locations are recommended to establish the validityof the results obtained.

scholarly journals Simulation of extreme rainfall and streamflow events in small Mediterranean watersheds with a one-way-coupled atmospheric–hydrologic modelling system

2020 ◽  
Vol 20 (10) ◽  
pp. 2791-2810
Author(s):  
Corrado Camera ◽  
Adriana Bruggeman ◽  
George Zittis ◽  
Ioannis Sofokleous ◽  
Joël Arnault
Keyword(s):  
Extreme Events ◽  
Model Performance ◽  
Extreme Rainfall ◽  
Climate Projections ◽  
Hydrologic Modelling ◽  
Extreme Rainfall Events ◽  
Mountain Watersheds ◽  
Small Watersheds ◽  
Set Up ◽  
The One

Abstract. Coupled atmospheric–hydrologic systems are increasingly used as instruments for flood forecasting and water management purposes, making the performance of the hydrologic routines a key indicator of the model functionality. This study's objectives were (i) to calibrate the one-way-coupled WRF-Hydro model for simulating extreme events in Cyprus with observed precipitation and (ii) to evaluate the model performance when forced with WRF-downscaled (1×1 km2) re-analysis precipitation data (ERA-Interim). This set-up resembles a realistic modelling chain for forecasting applications and climate projections. Streamflow was modelled during extreme rainfall events that occurred in January 1989 (calibration) and November 1994 (validation) over 22 mountain watersheds. In six watersheds, Nash–Sutcliffe efficiencies (NSEs) larger than 0.5 were obtained for both events. The WRF-modelled rainfall showed an average NSE of 0.83 for January 1989 and 0.49 for November 1994. Nevertheless, hydrologic simulations of the two events with the WRF-modelled rainfall and the calibrated WRF-Hydro returned negative streamflow NSE for 13 watersheds in January 1989 and for 18 watersheds in November 1994. These results indicate that small differences in amounts or shifts in time or space of modelled rainfall, in comparison with observed precipitation, can strongly modify the hydrologic response of small watersheds to extreme events. Thus, the calibration of WRF-Hydro for small watersheds depends on the availability of observed rainfall with high temporal and spatial resolution. However, the use of modelled precipitation input data will remain important for studying the effect of future extremes on flooding and water resources.

scholarly journals Enhancing the Output of Climate Models: A Weather Generator for Climate Change Impact Studies

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1074
Author(s):  
Pietro Croce ◽  
Paolo Formichi ◽  
Filippo Landi
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Performance Metrics ◽  
Climate Model ◽  
Weather Generator ◽  
Computational Time ◽  
Maximum Temperature ◽  
Climate Projections ◽  
Eastern Region ◽  
Climatic Data

Evaluation of effects of climate change on climate variable extremes is a key topic in civil and structural engineering, strongly affecting adaptation strategy for resilience. Appropriate procedures to assess the evolution over time of climatic actions are needed to deal with the inherent uncertainty of climate projections, also in view of providing more sound and robust predictions at the local scale. In this paper, an ad hoc weather generator is presented that is able to provide a quantification of climate model inherent uncertainties. Similar to other weather generators, the proposed algorithm allows the virtualization of the climatic data projection process, overcoming the usual limitations due to the restricted number of available climate model runs, requiring huge computational time. However, differently from other weather generation procedures, this new tool directly samples from the output of Regional Climate Models (RCMs), avoiding the introduction of additional hypotheses about the stochastic properties of the distributions of climate variables. Analyzing the ensemble of so-generated series, future changes of climatic actions can be assessed, and the associated uncertainties duly estimated, as a function of considered greenhouse gases emission scenarios. The efficiency of the proposed weather generator is discussed evaluating performance metrics and referring to a relevant case study: the evaluation of extremes of minimum and maximum temperature, precipitation, and ground snow load in a central Eastern region of Italy, which is part of the Mediterranean climatic zone. Starting from the model ensemble of six RCMs, factors of change uncertainty maps for the investigated region are derived concerning extreme daily temperatures, daily precipitation, and ground snow loads, underlying the potentialities of the proposed approach.

scholarly journals Validation of Terrestrial Biogeochemistry in CMIP6 Earth System Models: A Review

2021 ◽  
Author(s):  
Lynsay Spafford ◽  
Andrew Hugh MacDougall
Keyword(s):  
Land Surface ◽  
Model Comparison ◽  
Performance Metrics ◽  
Global Climate ◽  
Model Performance ◽  
Biogeochemical Cycle ◽  
Earth System ◽  
System Models ◽  
Experimental Protocols ◽  
Earth System Models

Abstract. The vital role of terrestrial biogeochemical cycles in influencing global climate change is explored by modelling groups internationally through Land Surface Models (LSMs) coupled to atmospheric and oceanic components within Earth System Models (ESMs). The sixth phase of the Coupled Model Intercomparison Project (CMIP6) provided an opportunity to compare ESM output by providing common forcings and experimental protocols. Despite these common experimental protocols, a variety of terrestrial biogeochemical cycle validation approaches were adopted by CMIP6 participants, leading to ambiguous model performance assessment and uncertainty attribution across ESMs. In this review we summarize current methods of terrestrial biogeochemical cycle validation utilized by CMIP6 participants and concurrent community model comparison studies. We focus on variables including: the dimensions of evaluations, observation-based reference datasets, and metrics of model performance. To ensure objective and thorough validations for the seventh phase of CMIP (CMIP7) we recommend the use of a standard validation protocol employing a broad suite of certainty-weighted observation-based reference datasets, targeted model performance metrics, and comparisons across a range of spatiotemporal scales.

scholarly journals Uncertainty of Concentration–Terrestrial Carbon Feedback in Earth System Models*

2014 ◽  
Vol 27 (9) ◽  
pp. 3425-3445 ◽  
Author(s):  
Tomohiro Hajima ◽  
Kaoru Tachiiri ◽  
Akihiko Ito ◽  
Michio Kawamiya
Keyword(s):  
Carbon Cycle ◽  
Co2 Concentration ◽  
Carbon Accumulation ◽  
Atmospheric Co2 ◽  
Climate Projections ◽  
Earth System ◽  
Terrestrial Carbon ◽  
System Models ◽  
Analytical Approaches ◽  
Earth System Models

Abstract Carbon uptake by land and ocean as a biogeochemical response to increasing atmospheric CO2 concentration is called concentration–carbon feedback and is one of the carbon cycle feedbacks of the global climate. This feedback can have a major impact on climate projections with an uncertain magnitude. This paper focuses on the concentration–carbon feedback in terrestrial ecosystems, analyzing the mechanisms and strength of the feedback reproduced by Earth system models (ESMs) participating in phase 5 of the Coupled Model Intercomparison Project. It is confirmed that multiple ESMs driven by a common scenario show a large spread of concentration–carbon feedback strength among models. Examining the behavior of the carbon fluxes and pools of the models showed that the sensitivity of plant productivity to elevated CO2 is likely the key to reduce the spread, although increasing CO2 stimulates other carbon cycle processes. Simulations with a single ESM driven by different CO2 pathways demonstrated that carbon accumulation increases in scenarios with slower CO2 increase rates. Using both numerical and analytical approaches, the study showed that the difference among CO2 scenarios is a time lag of terrestrial carbon pools in response to atmospheric CO2 increase—a high rate of CO2 increase results in smaller carbon accumulations than that in an equilibrium state of a given CO2 concentration. These results demonstrate that the current quantities for concentration–carbon feedback are incapable of capturing the feedback dependency on the carbon storage state and suggest that the concentration feedback can be larger for future scenarios where the CO2 growth rate is reduced.

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