266 research outputs found
Substantial Differences in Crop Yield Sensitivities Between Models Call for Functionality‐Based Model Evaluation
No full textAbstract
Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.Plain Language Summary
Crop models are widely used to compute crop yields under future climate change. Yields are determined by many interacting processes. Simulated future crop yields often show a broad uncertainty range. We investigate the sensitivity of nine different crop models to individual model inputs (carbon dioxide, temperature, water, nitrogen) in a very large simulation data set and find that there are substantial differences. We conclude that crop model evaluation needs to include analyses of functional properties to avoid that very diverse model responses to drivers are not tracked if interacting processes cancel out in the historical evaluation period but not in future scenarios, leading to large differences between models.Key Points
Crop models show strong differences in input sensitivities
Standardized modeling experiments reveal differences in emergent functional relationships
New standards in model evaluation are neededAbstract
Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.Plain Language Summary
Crop models are widely used to compute crop yields under future climate change. Yields are determined by many interacting processes. Simulated future crop yields often show a broad uncertainty range. We investigate the sensitivity of nine different crop models to individual model inputs (carbon dioxide, temperature, water, nitrogen) in a very large simulation data set and find that there are substantial differences. We conclude that crop model evaluation needs to include analyses of functional properties to avoid that very diverse model responses to drivers are not tracked if interacting processes cancel out in the historical evaluation period but not in future scenarios, leading to large differences between models.Key Points
Crop models show strong differences in input sensitivities
Standardized modeling experiments reveal differences in emergent functional relationships
New standards in model evaluation are neededAbstract
Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.Plain Language Summary
Crop models are widely used to compute crop yields under future climate change. Yields are determined by many interacting processes. Simulated future crop yields often show a broad uncertainty range. We investigate the sensitivity of nine different crop models to individual model inputs (carbon dioxide, temperature, water, nitrogen) in a very large simulation data set and find that there are substantial differences. We conclude that crop model evaluation needs to include analyses of functional properties to avoid that very diverse model responses to drivers are not tracked if interacting processes cancel out in the historical evaluation period but not in future scenarios, leading to large differences between models.Key Points
Crop models show strong differences in input sensitivities
Standardized modeling experiments reveal differences in emergent functional relationships
New standards in model evaluation are neededNational Science Board https://doi.org/10.13039/10000571
Climate Shifts Within Major Agricultural Seasons for +1.5 and +2.0 C Worlds: HAPPI Projections and AgMIP Modeling Scenarios
No full textThis study compares climate changes in major agricultural regions and current agricultural seasons associated with global warming of +1.5 or +2.0 C above pre-industrial conditions. It describes the generation of climate scenarios for agricultural modeling applications conducted as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments. Climate scenarios from the Half a degree Additional warming, Projections, Prognosis and Impacts project (HAPPI) are largely consistent with transient scenarios extracted from RCP4.5 simulations of the Coupled Model Intercomparison Project phase 5 (CMIP5). Focusing on food and agricultural systems and top-producing breadbaskets in particular, we distinguish maize, rice, wheat, and soy season changes from global annual mean climate changes. Many agricultural regions warm at a rate that is faster than the global mean surface temperature (including oceans) but slower than the mean land surface temperature, leading to regional warming that exceeds 0.5 C between the +1.5 and +2.0 C Worlds. Agricultural growing seasons warm at a pace slightly behind the annual temperature trends in most regions, while precipitation increases slightly ahead of the annual rate. Rice cultivation regions show reduced warming as they are concentrated where monsoon rainfall is projected to intensify, although projections are influenced by Asian aerosol loading in climate mitigation scenarios. Compared to CMIP5, HAPPI slightly underestimates the CO2 concentration that corresponds to the +1.5 C World but overestimates the CO2 concentration for the +2.0 C World, which means that HAPPI scenarios may also lead to an overestimate in the beneficial effects of CO2 on crops in the +2.0 C World. HAPPI enables detailed analysis of the shifting distribution of extreme growing season temperatures and precipitation, highlighting widespread increases in extreme heat seasons and heightened skewness toward hot seasons in the tropics. Shifts in the probability of extreme drought seasons generally tracked median precipitation changes; however, some regions skewed toward drought conditions even where median precipitation changes were small. Together, these findings highlight unique seasonal and agricultural region changes in the +1.5 C and +2.0 C worlds for adaptation planning in these climate stabilization targets
Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models
No full text201
NARR’s Atmospheric Water Cycle Components. Part I: 20-Year Mean and Annual Interactions
No full textAbstract
The North American Regional Reanalysis (NARR) atmospheric water cycle is examined from 1980 to 1999 using a budget approach, with a particular emphasis on annual component interactions and the role of hourly precipitation assimilation. NARR’s summertime atmospheric water cycle and diurnal component interactions are examined in Part II of this study. NARR’s high-resolution reanalysis and precipitation assimilation allow an improved climatology of mean water cycle components over North America, which is very attractive for applications, climate impact assessments, and as a basis for comparison with other products. A 20-yr climatology of precipitation, evaporation, moisture flux convergence, and the residual error term are produced for comparison to observations, other reanalyses and models, and future climate scenarios. Maps of the normalized covariance of annual precipitation with each of the other water cycle components identify regimes of seasonal interaction that form an additional basis for comparison. The annual cycle of assimilated precipitation is compared to high-resolution precipitation products as an example, and points of interest for continuing studies are identified. Analysis of the mean and transient balances reveals a significant effect from NARR’s precipitation assimilation scheme, which is investigated using an estimate of NARR’s underlying model precipitation (before assimilation), generated using the precipitation assimilation increment as a proxy. Biases of the precipitation assimilation scheme are then characterized spatially and temporally to inform the interpretation of NARR applications and comparisons. These model precipitation estimates reveal a more tightly closed atmospheric water cycle with predominantly excessive precipitation, resulting in too vigorous evaporation and moisture flux convergences. The sign and magnitude of evaporation and moisture flux convergence biases are found to be related to the precipitation assimilation correction and are important to consider in applications of NARR output.</jats:p
NARR’s Atmospheric Water Cycle Components. Part II: Summertime Mean and Diurnal Interactions
No full textAbstract
Summertime interactions in the North American Regional Reanalysis (NARR) atmospheric water cycle are examined from a user’s perspective over the 1980–99 period with a particular emphasis on the diurnal cycle, the nocturnal maximum of precipitation over the Midwest, and the impacts of precipitation assimilation. NARR’s full-year mean atmospheric water cycle and its annual variations are examined in Part I of this study. North American summertime (June–August) features substantial convective activity that is often organized on a diurnal scale, although diverse regional diurnal features are evident to various extents in high-resolution precipitation products. NARR’s hourly assimilation of precipitation observations over the continental United States allows it to resolve diurnal effects on the water cycle, but in other regions the diurnal cycle of precipitation is imposed from an external reanalysis model. The prominent nocturnal maximum in precipitation across the upper Midwest is captured in NARR, but different precipitation assimilation sources disrupt the propagation of convective systems across the Canadian border. Normalized covariances of NARR’s diurnal water cycle component interactions in the nocturnal maximum region reveal a strong relationship between moisture convergence and precipitation, and also measure the way in which the precipitable water column holds a lagged response between evaporation and precipitation. In many regions the diurnal cycle of rainfall is driven by interactions with water cycle components that differ from those driving the seasonal cycle. A comparison between NARR’s precipitation and an estimate of the model precipitation prior to precipitation assimilation distinguishes the portion of the water cycle captured in full by the model and that which is value added by the assimilation routine. The nocturnal rainfall maximum is not present in the model precipitation estimate, leading to diurnal-scale biases in the evaporation and moisture flux convergence fields that are not directly modified by precipitation assimilation.</jats:p
Climate Change Impacts on Agriculture: Challenges, Opportunities, and AgMIP Frameworks for Foresight
No full textAgricultural systems are currently undergoing rapid shifts owing to socioeconomic development, technological change, population growth, economic opportunity, evolving demand for commodities, and the need for sustainability amid global environmental change. It is not sufficient to maintain current harvest levels; rather, there is a need to rapidly increase production in light of a population growing to nearly 10 billion by mid-century and to more than 11 billion by 2100 (FAO, 2016; UN, 2016; Popkin et al., 2012). Current and future agricultural systems are additionally burdened by human-caused climate change, the result of accumulating greenhouse gas and aerosol emissions, ecological destruction, and land use changes that have altered the chemical composition of Earths atmosphere and trapped energy in the Earth system (IPCC, 2013; Porter et al., 2014). This increased energy has already raised average surface temperatures by approximately 1 degree Centigrade (GISTEMP Team, 2017; Hansen et al., 2010), leading early on to the term global warming, but this phenomenon is now more accurately referred to as climate change because it also modifies atmospheric circulation, adjusts regional and seasonal precipitation patterns, and shifts the distribution and characteristics of extreme events (Bindoff et al., 2013; Collins et al., 2013). Food and health systems face increasing risk owing to progressive climate change now manifesting itself as more frequent, severe extreme weather eventsheat waves, droughts, and floods (IPCC, 2013). Often without warning, weather-related shocks can have catastrophic and reverberating impacts on the increasingly exposed global food systemthrough production, processing, distribution, retail, disposal, and waste. Simultaneously, malnutrition and ill health are arising from lack of access to nutritious food, exacerbated in crises such as food price spikes or shortages. For some countries, particularly import-dependent low-income countries, weather shocks and price spikes can lead to social unrest, famine, and migration
The hazard components of representative key risks: the physical climate perspective
No full textThe framework of Representative Key Risks (RKRs) has been adopted by the Intergovernmental Panel on Climate Change Working Group II (WGII) to categorize, assess and communicate a wide range of regional and sectoral key risks from climate change. These are risks expected to become severe due to the potentially detrimental convergence of changing climate conditions with the exposure and vulnerability of human and natural systems. Other papers in this special issue treat each of eight RKRs holistically by assessing their current status and future evolution as a result of this convergence. However, in these papers, such assessment cannot always be organized according to a systematic gradation of climatic changes. Often the big-picture evolution of risk has to be extrapolated from either qualitative effects of “low”, “medium” and “high” warming, or limited/focused analysis of the consequences of particular mitigation choices (e.g., benefits of limiting warming to 1.5 or 2C), together with consideration of the socio-economic context and possible adaptation choices. In this study we offer a representation – as systematic as possible given current literature and assessments – of the future evolution of the hazard components of RKRs. We identify the relevant hazards for each RKR, based upon the WGII authors’ assessment, and we report on their current state and expected future changes in magnitude, intensity and/or frequency, linking these changes to Global Warming Levels (GWLs) to the extent possible. We draw on the assessment of changes in climatic impact-drivers relevant to RKRs described in the 6th Assessment Report by Working Group I supplemented when needed by more recent literature. For some of these quantities - like regional trends in oceanic and atmospheric temperature and precipitation, some heat and precipitation extremes, permafrost thaw and Northern Hemisphere snow cover - a strong and quantitative relationship with increasing GWLs has been identified. For others - like frequency and intensity of tropical cyclones and extra-tropical storms, and fire weather - that link can only be described qualitatively. For some processes - like the behavior of ice sheets, or changes in circulation dynamics - large uncertainties about the effects of different GWLs remain, and for a few others - like ocean pH and air pollution - the composition of the scenario of anthropogenic emissions is most relevant, rather than the warming reached. In almost all cases, however, the basic message remains that every small increment in CO
2 concentration in the atmosphere and associated warming will bring changes in climate phenomena that will contribute to increasing risk of impacts on human and natural systems, in the absence of compensating changes in these systems’ exposure and vulnerability, and in the absence of effective adaptation. Our picture of the evolution of RKR-relevant climatic impact-drivers complements and enriches the treatment of RKRs in the other papers in at least two ways: by filling in their often only cursory or limited representation of the physical climate aspects driving impacts, and by providing a fuller representation of their future potential evolution, an important component – if never the only one – of the future evolution of risk severity.
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Lessons from climate modeling on the design and use of ensembles for crop modeling
No full textWorking with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor
Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments
No full textThe Agricultural Model Intercomparison and Improvement Project (AgMIP) has developed novel methods for Coordinated Global and Regional Assessments (CGRA) of agriculture and food security in a changing world. The present study aims to perform a proof of concept of the CGRA to demonstrate advantages and challenges of the proposed framework. This effort responds to the request by the UN Framework Convention on Climate Change (UNFCCC) for the implications of limiting global temperature increases to 1.5°C and 2.0°C above pre-industrial conditions. The CGRA consistently links disciplines, models and scales in order to track the complex chain of climate impacts and identify key vulnerabilities, feedbacks and uncertainties in managing future risk. CGRA proof-of-concept results show that, at the global scale, there are mixed areas of positive and negative simulated wheat and maize yield changes, with declines in some breadbasket regions, at both 1.5°C and 2.0°C
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