scholarly journals Projected Response of Tropical Cyclone Intensity and Intensification in a Global Climate Model

2018 ◽  
Vol 31 (20) ◽  
pp. 8281-8303 ◽  
Author(s):  
Kieran Bhatia ◽  
Gabriel Vecchi ◽  
Hiroyuki Murakami ◽  
Seth Underwood ◽  
James Kossin
Keyword(s):  
Climate Change ◽  
Radiative Forcing ◽  
Climate Models ◽  
Control Experiment ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
First Century ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Twenty First Century

As one of the first global coupled climate models to simulate and predict category 4 and 5 (Saffir–Simpson scale) tropical cyclones (TCs) and their interannual variations, the High-Resolution Forecast-Oriented Low Ocean Resolution (HiFLOR) model at the Geophysical Fluid Dynamics Laboratory (GFDL) represents a novel source of insight on how the entire TC intensification distribution could be transformed because of climate change. In this study, three 70-yr HiFLOR experiments are performed to identify the effects of climate change on TC intensity and intensification. For each of the experiments, sea surface temperature (SST) is nudged to different climatological targets and atmospheric radiative forcing is specified, allowing us to explore the sensitivity of TCs to these conditions. First, a control experiment, which uses prescribed climatological ocean and radiative forcing based on observations during the years 1986–2005, is compared to two observational records and evaluated for its ability to capture the mean TC behavior during these years. The simulated intensification distributions as well as the percentage of TCs that become major hurricanes show similarities with observations. The control experiment is then compared to two twenty-first-century experiments, in which the climatological SSTs from the control experiment are perturbed by multimodel projected SST anomalies and atmospheric radiative forcing from either 2016–35 or 2081–2100 (RCP4.5 scenario). The frequency, intensity, and intensification distribution of TCs all shift to higher values as the twenty-first century progresses. HiFLOR’s unique response to climate change and fidelity in simulating the present climate lays the groundwork for future studies involving models of this type.

scholarly journals A Comparison of Tropical Cyclone Projections in a High-resolution Global Climate Model and from Downscaling by Statistical and Statistical-deterministic Methods

2021 ◽  
pp. 1-48
Author(s):  
Renzhi Jing ◽  
Ning Lin ◽  
Kerry Emanuel ◽  
Gabriel Vecchi ◽  
Thomas R. Knutson
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Tropical Cyclone ◽  
Deterministic Model ◽  
Global Climate Model ◽  
First Century ◽  
Return Level ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Moderate Increase ◽  
Twenty First Century

AbstractIn this study, we investigate the response of tropical cyclones (TCs) to climate change by using the Princeton environment-dependent probabilistic tropical cyclone (PepC) model and a statistical-deterministic method to downscale TCs using environmental conditions obtained from the Geophysical Fluid Dynamics Laboratory (GFDL) High-resolution Forecast-oriented Low Ocean Resolution (HiFLOR) model, under the Representative Concentration Pathway 4.5 (RCP4.5) emissions scenario for the North Atlantic basin. The downscaled TCs for the historical climate (1986-2005) are compared with those in the mid- (2016-35) and late-twenty-first century (2081-2100). The downscaled TCs are also compared with TCs explicitly simulated in HiFLOR. We show that while significantly more storms are detected in HiFLOR towards the end of the twenty-first century, the statistical-deterministic model projects a moderate increase in TC frequency, and PepC projects almost no increase in TC frequency. The changes in storm frequency in all three datasets are not significant in the mid-twenty-first century. All three project that storms will become more intense and the fraction of major hurricanes and Category 5 storms will significantly increase in the future climates. However, HiFLOR projects the largest increase in intensity while PepC projects the least. The results indicate that HiFLOR’s TC projection is more sensitive to climate change effects and statistical models are less sensitive. Nevertheless, in all three datasets, storm intensification and frequency increase lead to relatively small changes in TC threat as measured by the return level of landfall intensity.

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.

How detailed do cloud microphysics need to be in climate models?

2021 ◽  
Author(s):  
Ulrike Proske ◽  
Sylvaine Ferrachat ◽  
David Neubauer ◽  
Ulrike Lohmann
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Computing Time ◽  
Cloud Microphysics ◽  
Model Response ◽  
Microphysical Processes ◽  
Improved Accuracy

<p>Clouds are of major importance for the climate system, but the radiative forcing resulting from their interaction with aerosols remains uncertain. To improve the representation of clouds in climate models, the parameterisations of cloud microphysical processes (CMPs) have become increasingly detailed. However, more detailed climate models do not necessarily result in improved accuracy for estimates of radiative forcing (Knutti and Sedláček, 2013; Carslaw et al., 2018). On the contrary, simpler formulations are cheaper, sufficient for some applications, and allow for an easier understanding of the respective process' effect in the model.</p><p>This study aims to gain an understanding which CMP parameterisation complexity is sufficient through simplification. We gradually phase out processes such as riming or aggregation from the global climate model ECHAM-HAM, meaning that the processes are only allowed to exhibit a fraction of their effect on the model state. The shape of the model response as a function of the artificially scaled effect of a given process helps to understand the importance of this process for the model response and its potential for simplification. For example, if partially removing a process induces only minor alterations in the present day climate, this process presents as a good candidate for simplification. This may be then further investigated, for example in terms of computing time.<br>The resulting sensitivities to CMP complexity are envisioned to guide CMP model simplifications as well as steer research towards those processes where a more accurate representation proves to be necessary.</p><p> </p><p><br>Carslaw, Kenneth, Lindsay Lee, Leighton Regayre, and Jill Johnson (Feb. 2018). “Climate Models Are Uncertain, but We Can Do Something About It”. In: Eos 99. doi: 10.1029/2018EO093757</p><p>Knutti, Reto and Jan Sedláček (Apr. 2013). “Robustness and Uncertainties in the New CMIP5 Climate Model Projections”. In: Nature Climate Change 3.4, pp. 369–373. doi: 10.1038/nclimate1716</p>

scholarly journals Quantification of the Expected Changes in Annual Maximum Daily Precipitation Quantiles under Climate Change in the Iberian Peninsula

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 23 ◽  
Author(s):  
Carlos Garijo ◽  
Luis Mediero
Keyword(s):  
Climate Change ◽  
Iberian Peninsula ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Control Period ◽  
Regional Climate Models ◽  
The Future

Climate model projections can be used to assess the expected behaviour of extreme precipitations in the future due to climate change. The European part of the Coordinated Regional Climate Downscalling Experiment (EURO-CORDEX) provides precipitation projections for the future under various representative concentration pathways (RCPs) through regionalised Global Climate Model (GCM) outputs by a set of Regional Climate Models (RCMs). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX are analysed for the Iberian Peninsula. Precipitation quantiles for a set of probabilities of non-exceedance are estimated by using the Generalized Extreme Value (GEV) distribution and L-moments. Precipitation quantiles expected in the future are compared with the precipitation quantiles in the control period for each climate model. An approach based on Monte Carlo simulations is developed in order to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period. Thus, statistically significant changes are identified. The higher the significance threshold, the fewer cells with significant changes are identified. Consequently, a set of maps are obtained in order to assist the decision-making process in subsequent climate change studies.

scholarly journals Projection of Global Wave Climate Change toward the End of the Twenty-First Century

2012 ◽  
Vol 26 (21) ◽  
pp. 8269-8288 ◽  
Author(s):  
Alvaro Semedo ◽  
Ralf Weisse ◽  
Arno Behrens ◽  
Andreas Sterl ◽  
Lennart Bengtsson ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Global Climate Model ◽  
Wave Climate ◽  
Ocean Model ◽  
First Century ◽  
Climate Projections ◽  
Moderate Increase ◽  
Intergovernmental Panel ◽  
Twenty First Century

Abstract Wind-generated waves at the sea surface are of outstanding importance for both their practical relevance in many aspects, such as coastal erosion, protection, or safety of navigation, and for their scientific relevance in modifying fluxes at the air–sea interface. So far, long-term changes in ocean wave climate have been studied mostly from a regional perspective with global dynamical studies emerging only recently. Here a global wave climate study is presented, in which a global wave model [Wave Ocean Model (WAM)] is driven by atmospheric forcing from a global climate model (ECHAM5) for present-day and potential future climate conditions represented by the Intergovernmental Panel for Climate Change (IPCC) A1B emission scenario. It is found that changes in mean and extreme wave climate toward the end of the twenty-first century are small to moderate, with the largest signals being a poleward shift in the annual mean and extreme significant wave heights in the midlatitudes of both hemispheres, more pronounced in the Southern Hemisphere and most likely associated with a corresponding shift in midlatitude storm tracks. These changes are broadly consistent with results from the few studies available so far. The projected changes in the mean wave periods, associated with the changes in the wave climate in the middle to high latitudes, are also shown, revealing a moderate increase in the equatorial eastern side of the ocean basins. This study presents a step forward toward a larger ensemble of global wave climate projections required to better assess robustness and uncertainty of potential future wave climate change.

scholarly journals Projecting flood hazard under climate change: an alternative approach to model chains

2013 ◽  
Vol 1 (6) ◽  
pp. 7357-7385 ◽  
Author(s):  
J. M. Delgado ◽  
B. Merz ◽  
H. Apel
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Emission Scenario ◽  
Flood Hazard ◽  
Global Climate ◽  
Global Climate Model ◽  
Flood Frequency ◽  
Frequency Model ◽  
Alternative Approach

Abstract. Flood hazard projections under climate change are typically derived by applying model chains consisting of the following elements: "emission scenario – global climate model – downscaling, possibly including bias correction – hydrological model – flood frequency analysis". To date, this approach yields very uncertain results, due to the difficulties of global and regional climate models to represent precipitation. The implementation of such model chains requires large efforts, and their complexity is high. We propose for the Mekong River an alternative approach which is based on a shortened model chain: "emission scenario – global climate model – non-stationary flood frequency model". The underlying idea is to use a link between the Western Pacific monsoon and local flood characteristics: the variance of the monsoon drives a nonstationary flood frequency model, yielding a direct estimate of flood probabilities. This approach bypasses the uncertain precipitation, since the monsoon variance is derived from large-scale wind fields which are better represented by climate models. The simplicity of the monsoon-flood link allows deriving large ensembles of flood projections under climate change. We conclude that this is a worthwhile, complementary approach to the typical model chains in catchments where a substantial link between climate and floods is found.

scholarly journals An Investigation of cold-wet Compound Events in Greece

2021 ◽  
Author(s):  
Iason Markantonis ◽  
Diamando Vlachogiannis ◽  
Thanasis Sfetsos ◽  
Ioannis Kioutsioukis ◽  
Nadia Politi
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Extreme Event ◽  
Global Climate ◽  
Global Climate Model ◽  
Horizontal Resolution ◽  
Daily Minimum Temperature ◽  
Historical Period ◽  
Compound Events

<p>Climate change is set to affect extreme climate and meteorological events. The combination of interacting physical processes (climate drivers) across various spatial and temporal scales resulting to an extreme event is referred to as compound event. So far, climate change impacts on compound events in Greece such as daily cold-wet events have not been explored. The complex geography and topography of Greece forms a variety of regions with different local climate and a great range in daily minimum temperature and precipitation distributions. This leads to the assumption that there we will also observe a variety in the distribution of cold-wet events depending on the region. Aim of our study in this work is first to identify the cold-wet events based on observational data and then to examine the predictive capability of regional different climate models and ERA-Interim against observations from the Hellenic National Meteorological Service (HNMS) stations for the occurrence of cold-wet compound events in the present climate. The study will focus on the colder and wetter period of the year (November-April) to determine the extremes for this period. Specifically, the datasets employed are from two EURO-CORDEX Regional Climate Models (RCMs) with 0.11° horizontal resolution and validated ERA-Interim Reanalysis downscaled with the Weather Research and Forecasting (WRF) model at 5km horizontal resolution, for the historical period 1980-2004. In particular, the RCM datasets analyses have been produced from SMHI-RCA4 driven by MPI-M-MPI-ESM-LR Global Climate Model (GCM) and CLMcom-CLM-CCLM4-8-17 driven by MOHC-HadGEM2-ES GCM. After the comparison with the observations, the gridded data from the models will give us the ability to observe the spatial distribution of the compound events.</p>

scholarly journals Projecting flood hazard under climate change: an alternative approach to model chains

2014 ◽  
Vol 14 (6) ◽  
pp. 1579-1589 ◽  
Author(s):  
J. M. Delgado ◽  
B. Merz ◽  
H. Apel
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Emission Scenario ◽  
Flood Hazard ◽  
Global Climate ◽  
Global Climate Model ◽  
Flood Frequency ◽  
Frequency Model ◽  
Alternative Approach

Abstract. Flood hazard projections under climate change are typically derived by applying model chains consisting of the following elements: "emission scenario – global climate model – downscaling, possibly including bias correction – hydrological model – flood frequency analysis". To date, this approach yields very uncertain results, due to the difficulties of global and regional climate models to represent precipitation. The implementation of such model chains requires major efforts, and their complexity is high. We propose for the Mekong River an alternative approach which is based on a shortened model chain: "emission scenario – global climate model – non-stationary flood frequency model". The underlying idea is to use a link between the Western Pacific monsoon and local flood characteristics: the variance of the monsoon drives a non-stationary flood frequency model, yielding a direct estimate of flood probabilities. This approach bypasses the uncertain precipitation, since the monsoon variance is derived from large-scale wind fields which are better represented by climate models. The simplicity of the monsoon–flood link allows deriving large ensembles of flood projections under climate change. We conclude that this is a worthwhile, complementary approach to the typical model chains in catchments where a substantial link between climate and floods is found.

scholarly journals Detection and Attribution of Multivariate Climate Change Signals Using Discriminant Analysis and Bayesian Theorem

2017 ◽  
Vol 30 (19) ◽  
pp. 7757-7776
Author(s):  
Heiko Paeth ◽  
Felix Pollinger ◽  
Christoph Ring
Keyword(s):  
Climate Change ◽  
Discriminant Analysis ◽  
Radiative Forcing ◽  
Climate Model ◽  
First Century ◽  
Climate Indices ◽  
Bayesian Theorem ◽  
Climate Anomalies ◽  
Detection And Attribution ◽  
Twenty First Century

Abstract Detection and attribution methods in climatological research aim at assessing whether observed climate anomalies and trends are still consistent with the range of natural climate variations or rather an indication of anthropogenic climate change. In this study, the authors pursue a novel approach by using discriminant analysis to enhance the distinction between past and future climates from state-of-the-art climate model simulations. The method is based on multivariate fingerprints that are defined in the space of several prominent climate indices representing the thermal, dynamical, and hygric aspects of climate change. Attribution is carried out by means of a Bayesian classification approach. The leading discriminant function accounts for more than 99% of total discriminability, with temperature variables, extratropical precipitation, and extratropical circulation modes mainly contributing to the discriminant power. The misclassification probability between probability density functions of past and future climates is substantially reduced by the discriminant analysis: from >50% to <15%. Since the mid-1980s, the observed anomalies of the considered climate indices are more or less consistently attributed to a climate under strong radiative forcing, projected for the first half of the twenty-first century. The authors also assess the sensitivity of their results to different emissions scenarios from the CMIP3 and CMIP5 multimodel ensembles, seasons, prior probabilities for the early twenty-first-century climate, estimates of the observational error, low-pass filters, variable compositions, group numbers, and reference data.

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