scholarly journals Assessing the impact of global warming on worldwide open field tomato cultivation through CSIRO-Mk3·0 global climate model

2016 ◽  
Vol 155 (3) ◽  
pp. 407-420 ◽  
Author(s):  
R. S. SILVA ◽  
L. KUMAR ◽  
F. SHABANI ◽  
M. C. PICANÇO
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Open Field ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Current Model ◽  
Global Climate Models ◽  
The Impact

SUMMARYTomato (Solanum lycopersicum L.) is one of the most important vegetable crops globally and an important agricultural sector for generating employment. Open field cultivation of tomatoes exposes the crop to climatic conditions, whereas greenhouse production is protected. Hence, global warming will have a greater impact on open field cultivation of tomatoes rather than the controlled greenhouse environment. Although the scale of potential impacts is uncertain, there are techniques that can be implemented to predict these impacts. Global climate models (GCMs) are useful tools for the analysis of possible impacts on a species. The current study aims to determine the impacts of climate change and the major factors of abiotic stress that limit the open field cultivation of tomatoes in both the present and future, based on predicted global climate change using CLIMatic indEX and the A2 emissions scenario, together with the GCM Commonwealth Scientific and Industrial Research Organisation (CSIRO)-Mk3·0 (CS), for the years 2050 and 2100. The results indicate that large areas that currently have an optimum climate will become climatically marginal or unsuitable for open field cultivation of tomatoes due to progressively increasing heat and dry stress in the future. Conversely, large areas now marginal and unsuitable for open field cultivation of tomatoes will become suitable or optimal due to a decrease in cold stress. The current model may be useful for plant geneticists and horticulturalists who could develop new regional stress-resilient tomato cultivars based on needs related to these modelling projections.

Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model

2008 ◽  
Vol 366 (1875) ◽  
pp. 2559-2577 ◽  
Author(s):  
J Berner ◽  
F.J Doblas-Reyes ◽  
T.N Palmer ◽  
G Shutts ◽  
A Weisheimer
Keyword(s):  
Cellular Automaton ◽  
Systematic Error ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Seasonal Prediction ◽  
Global Climate Models ◽  
Seasonal Forecasts ◽  
The Impact

The impact of a nonlinear dynamic cellular automaton (CA) model, as a representation of the partially stochastic aspects of unresolved scales in global climate models, is studied in the European Centre for Medium Range Weather Forecasts coupled ocean–atmosphere model. Two separate aspects are discussed: impact on the systematic error of the model, and impact on the skill of seasonal forecasts. Significant reductions of systematic error are found both in the tropics and in the extratropics. Such reductions can be understood in terms of the inherently nonlinear nature of climate, in particular how energy injected by the CA at the near-grid scale can backscatter nonlinearly to larger scales. In addition, significant improvements in the probabilistic skill of seasonal forecasts are found in terms of a number of different variables such as temperature, precipitation and sea-level pressure. Such increases in skill can be understood both in terms of the reduction of systematic error as mentioned above, and in terms of the impact on ensemble spread of the CA's representation of inherent model uncertainty.

scholarly journals High-end climate change impact on European runoff and low flows – exploring the effects of forcing biases

2016 ◽  
Vol 20 (5) ◽  
pp. 1785-1808 ◽  
Author(s):  
Lamprini V. Papadimitriou ◽  
Aristeidis G. Koutroulis ◽  
Manolis G. Grillakis ◽  
Ioannis K. Tsanis
Keyword(s):  
Global Warming ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Data Set ◽  
Low Flows ◽  
The Impact ◽  
Selection Of

Abstract. Climate models project a much more substantial warming than the 2 °C target under the more probable emission scenarios, making higher-end scenarios increasingly plausible. Freshwater availability under such conditions is a key issue of concern. In this study, an ensemble of Euro-CORDEX projections under RCP8.5 is used to assess the mean and low hydrological states under +4 °C of global warming for the European region. Five major European catchments were analysed in terms of future drought climatology and the impact of +2 °C versus +4 °C global warming was investigated. The effect of bias correction of the climate model outputs and the observations used for this adjustment was also quantified. Projections indicate an intensification of the water cycle at higher levels of warming. Even for areas where the average state may not considerably be affected, low flows are expected to reduce, leading to changes in the number of dry days and thus drought climatology. The identified increasing or decreasing runoff trends are substantially intensified when moving from the +2 to the +4° of global warming. Bias correction resulted in an improved representation of the historical hydrology. It is also found that the selection of the observational data set for the application of the bias correction has an impact on the projected signal that could be of the same order of magnitude to the selection of the Global Climate Model (GCM).

Global hydrology modelling and uncertainty: running multiple ensembles with a campus grid

2010 ◽  
Vol 368 (1926) ◽  
pp. 4005-4021 ◽  
Author(s):  
Simon N. Gosling ◽  
Dan Bretherton ◽  
Keith Haines ◽  
Nigel W. Arnell
Keyword(s):  
Climate Change ◽  
Water Resource ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Models ◽  
Resource Stress ◽  
Runoff Changes ◽  
Campus Grid ◽  
The Impact

Uncertainties associated with the representation of various physical processes in global climate models (GCMs) mean that, when projections from GCMs are used in climate change impact studies, the uncertainty propagates through to the impact estimates. A complete treatment of this ‘climate model structural uncertainty’ is necessary so that decision-makers are presented with an uncertainty range around the impact estimates. This uncertainty is often underexplored owing to the human and computer processing time required to perform the numerous simulations. Here, we present a 189-member ensemble of global river runoff and water resource stress simulations that adequately address this uncertainty. Following several adaptations and modifications, the ensemble creation time has been reduced from 750 h on a typical single-processor personal computer to 9 h of high-throughput computing on the University of Reading Campus Grid. Here, we outline the changes that had to be made to the hydrological impacts model and to the Campus Grid, and present the main results. We show that, although there is considerable uncertainty in both the magnitude and the sign of regional runoff changes across different GCMs with climate change, there is much less uncertainty in runoff changes for regions that experience large runoff increases (e.g. the high northern latitudes and Central Asia) and large runoff decreases (e.g. the Mediterranean). Furthermore, there is consensus that the percentage of the global population at risk to water resource stress will increase with climate change.

scholarly journals AMOC-emulator M-AMOC1.0 for uncertainty assessment of future projections

2016 ◽  
Author(s):  
Pepijn Bakker ◽  
Andreas Schmittner
Keyword(s):  
Global Warming ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Uncertainty Assessment ◽  
Global Climate Models ◽  
Meridional Overturning Circulation

Abstract. State-of-the-science global climate models show that global warming is likely to weaken the Atlantic Meridional Overturning Circulation (AMOC). While such models are arguably the best tools to perform AMOC projections, they do not allow a comprehensive uncertainty assessment because of limited computational resources. Here we present an AMOC-emulator, a box model with a number of free parameters that can be tuned to mimic the sensitivity of the AMOC to climate change of a specific global climate model. The AMOC-emulator (M-AMOC1.0) is applied to simulations of global warming and melting of the Greenland Ice Sheet, performed with an intermediate complexity model. Predictive power of the AMOC-emulator is shown by comparison with a number of additional warming and Greenland Ice Sheet melt scenario that have not been used in the tuning of the AMOC-emulator, highlighting the potential of the AMOC-emulator to assess the uncertainty in AMOC projections.

scholarly journals Downscaling the climate change for oceans around Australia

2012 ◽  
Vol 5 (1) ◽  
pp. 425-458 ◽  
Author(s):  
M. A. Chamberlain ◽  
C. Sun ◽  
R. J. Matear ◽  
M. Feng ◽  
S. J. Phipps
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Ocean Model ◽  
Global Climate Models ◽  
Global Ocean ◽  
Coupled Models

Abstract. At present, global climate models used to project changes in climate do not resolve mesoscale ocean features such as boundary currents and eddies. These missing features may be important to realistically project the marine impacts of climate change. Here we present a framework for dynamically downscaling coarse climate change projections utilising a global ocean model that resolves these features in the Australian region. The downscaling model used here is ocean-only. The ocean feedback on the air-sea fluxes is explored by restoring to surface temperature and salinity, as well as a calculated feedback to wind stress. These feedback approximations do not replace the need for fully coupled models, but they allow us to assess the sensitivity of the ocean in downscaled climate change simulations. Significant differences are found in sea surface temperature, salinity, stratification and transport between the downscaled projections and those of the climate model. While the magnitude of the climate change differences may vary with the feedback parameterisation used, the patterns of the climate change differences are consistent and develop rapidly indicating they are mostly independent of feedback that ocean differences may have on the air-sea fluxes. Until such a time when it is feasible to regularly run a global climate model with eddy resolution, our framework for ocean climate change downscaling provides an attractive way to explore how climate change may affect the mesoscale ocean environment.

scholarly journals Assessing the potential for simplification in global climate model cloud microphysics

2021 ◽  
Author(s):  
Ulrike Proske ◽  
Sylvaine Ferrachat ◽  
David Neubauer ◽  
Martin Staab ◽  
Ulrike Lohmann
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Cloud Microphysics ◽  
Global Climate Models ◽  
Ice Crystals ◽  
Model Output ◽  
Radiative Balance ◽  
The Impact

Abstract. Cloud properties and their evolution influence Earth's radiative balance. The cloud microphysical (CMP) processes that shape these properties are therefore important to be represented in global climate models. Historically, parameterizations in these models have grown more detailed and complex. However, a simpler formulation of CMP processes may leave the model results mostly unchanged while enabling an easier interpretation of model results and helping to increase process understanding. This study employs sensitivity analysis on an emulated perturbed parameter ensemble of the global aerosol-climate model ECHAM-HAM to illuminate the impact of selected CMP cloud ice processes on model output. The response to the phasing of a process thereby serves as a proxy for the effect of a simplification. Aggregation of ice crystals is found to be the dominant CMP process in influencing key variables such as the ice water path or cloud radiative effects, while riming of cloud droplets on snow influences mostly the liquid phase. Accretion of ice and snow and self-collection of ice crystals have a negligible influence on model output and are therefore identified as suitable candidates for future simplifications. In turn, the dominating role of aggregation suggests that this process has the greatest need to be represented correctly. A seasonal and spatially resolved analysis employing a spherical harmonics expansion of the data corroborates the results. This study introduces a new framework to evaluate a processes' impact in a complex numerical model, and paves the way for simplifications of CMP processes leading to more interpretable climate models.

scholarly journals Future rainfall and temperature changes in Brazil under global warming levels of 1.5ºC, 2ºC and 4ºC

2020 ◽  
Vol 11 (3) ◽  
pp. 57-90
Author(s):  
Diego Jatobá dos Santos ◽  
George Ulguim Pedra ◽  
Marcelo Guatura Barbosa da Silva ◽  
Carlos Augusto Guimarães Júnior ◽  
Lincoln Muniz Alves ◽  
...  
Keyword(s):  
Global Warming ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Climate Impacts ◽  
Global Climate Models ◽  
Northeastern Brazil ◽  
Climate Indices ◽  
Sea Ice Concentration

The present study analyzes the impacts of global warming of 1.5ºC, 2ºC, and 4ºC above pre-industrial levels in the Brazilian territory. Climate change projected among the different global warming levels has been analyzed for rainfall, temperature and extreme climate indices. The projections are derived from the global climate model HadGEM3-A, from the High-End cLimate Impacts and eXtremes (HELIX) international project, from the United Kingdom, forced by sea surface temperature and sea ice concentration of a subset of six CMIP5 (Coupled Model Intercomparison Project phase 5) global climate models and considering the RCP 8.5 (Representative Concentration Pathways) emissions scenario throughout the 21st century. Projections indicate robust differences in regional climate characteristics. These differences include changes: in the minimum and maximum air temperature close to the surface to all the country’s regions, in extremes of heat, particularly in northern Brazil, in the occurrence of heavy rainfall (Southern and Southeastern regions), and in the probability of droughts and rain deficits in some regions (Northern and Northeastern Brazil).

scholarly journals Strategy for generation of climate change projections feeding Spanish impact community

2018 ◽  
Vol 15 ◽  
pp. 217-230
Author(s):  
María Pilar Amblar-Francés ◽  
María Asunción Pastor-Saavedra ◽  
María Jesús Casado-Calle ◽  
Petra Ramos-Calzado ◽  
Ernesto Rodríguez-Camino
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Model ◽  
National Level ◽  
Global Climate Models ◽  
Climate Change Scenarios ◽  
Climate Change Projections

Abstract. Over the past decades, the successive Coupled Model Intercomparison Projects (CMIPs) have produced a huge amount of global climate model simulations. Along these years, the climate models have advanced and can thus provide credible evolution of climate at least at continental or global scales since they are better representing physical processes and feedbacks in the climate system. Nevertheless, due to the coarse horizontal resolution of global climate models, it is necessary to downscale these results for their use to assess possible future impacts of climate change in climate sensitive ecosystems and sectors and to adopt adaptation strategies at local and national level. In this vein, the Spanish State Meteorological Agency (AEMET) has been producing since 2006 a set of reference downscaled climate change projections over Spain either applying statistical downscaling techniques to the outputs of the Global Climate Models (GCMs) or making use of the information generated by dynamical downscaling techniques through European projects or international initiatives such as PRUDENCE, ENSEMBLES and EURO-CORDEX. The AEMET strategy aims at exploiting all the available sources of information on climate change projections. The generalized use of statistical and dynamical downscaling approaches allow us to encompass a great number of global models and therefore to provide a better estimation of uncertainty. Most impact climate change studies over Spain make use of this reference downscaled projections emphasizing the estimation of uncertainties. Additionally to the rationale and history behind the AEMET generation of climate change scenarios, we focus on some preliminary analysis of the dependency of estimated uncertainties on the different sources of data.

scholarly journals Regions of intensification of extreme snowfall under future warming

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lennart Quante ◽  
Sven N. Willner ◽  
Robin Middelanis ◽  
Anders Levermann
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Observational Data ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Climate Models ◽  
Average Intensity ◽  
High Latitudes ◽  
Future Warming

AbstractDue to climate change the frequency and character of precipitation are changing as the hydrological cycle intensifies. With regards to snowfall, global warming has two opposing influences; increasing humidity enables intense snowfall, whereas higher temperatures decrease the likelihood of snowfall. Here we show an intensification of extreme snowfall across large areas of the Northern Hemisphere under future warming. This is robust across an ensemble of global climate models when they are bias-corrected with observational data. While mean daily snowfall decreases, both the 99th and the 99.9th percentiles of daily snowfall increase in many regions in the next decades, especially for Northern America and Asia. Additionally, the average intensity of snowfall events exceeding these percentiles as experienced historically increases in many regions. This is likely to pose a challenge to municipalities in mid to high latitudes. Overall, extreme snowfall events are likely to become an increasingly important impact of climate change in the next decades, even if they will become rarer, but not necessarily less intense, in the second half of the century.

The MJO-QBO Relationship in a GCM with Stratospheric Nudging

2021 ◽  
pp. 1-69
Author(s):  
Zane Martin ◽  
Clara Orbe ◽  
Shuguang Wang ◽  
Adam Sobel
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Boreal Winter ◽  
Quasi Biennial Oscillation ◽  
Meridional Winds ◽  
Goddard Institute ◽  
Minimal Bias

AbstractObservational studies show a strong connection between the intraseasonal Madden-Julian oscillation (MJO) and the stratospheric quasi-biennial oscillation (QBO): the boreal winter MJO is stronger, more predictable, and has different teleconnections when the QBO in the lower stratosphere is easterly versus westerly. Despite the strength of the observed connection, global climate models do not produce an MJO-QBO link. Here the authors use a current-generation ocean-atmosphere coupled NASA Goddard Institute for Space Studies global climate model (Model E2.1) to examine the MJO-QBO link. To represent the QBO with minimal bias, the model zonal mean stratospheric zonal and meridional winds are relaxed to reanalysis fields from 1980-2017. The model troposphere, including the MJO, is allowed to freely evolve. The model with stratospheric nudging captures QBO signals well, including QBO temperature anomalies. However, an ensemble of nudged simulations still lacks an MJO-QBO connection.

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