Evaluating the Consistency between Statistically Downscaled and Global Dynamical Model Climate Change Projections

2008 ◽  
Vol 21 (22) ◽  
pp. 6052-6059 ◽  
Author(s):  
B. Timbal ◽  
P. Hope ◽  
S. Charles
Keyword(s):  
Global Warming ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Specific Humidity ◽  
Model Output ◽  
Winter Rainfall ◽  
Direct Model ◽  
The Impact

Abstract The consistency between rainfall projections obtained from direct climate model output and statistical downscaling is evaluated. Results are averaged across an area large enough to overcome the difference in spatial scale between these two types of projections and thus make the comparison meaningful. Undertaking the comparison using a suite of state-of-the-art coupled climate models for two forcing scenarios presents a unique opportunity to test whether statistical linkages established between large-scale predictors and local rainfall under current climate remain valid in future climatic conditions. The study focuses on the southwest corner of Western Australia, a region that has experienced recent winter rainfall declines and for which climate models project, with great consistency, further winter rainfall reductions due to global warming. Results show that as a first approximation the magnitude of the modeled rainfall decline in this region is linearly related to the model global warming (a reduction of about 9% per degree), thus linking future rainfall declines to future emission paths. Two statistical downscaling techniques are used to investigate the influence of the choice of technique on projection consistency. In addition, one of the techniques was assessed using different large-scale forcings, to investigate the impact of large-scale predictor selection. Downscaled and direct model projections are consistent across the large number of models and two scenarios considered; that is, there is no tendency for either to be biased; and only a small hint that large rainfall declines are reduced in downscaled projections. Among the two techniques, a nonhomogeneous hidden Markov model provides greater consistency with climate models than an analog approach. Differences were due to the choice of the optimal combination of predictors. Thus statistically downscaled projections require careful choice of large-scale predictors in order to be consistent with physically based rainfall projections. In particular it was noted that a relative humidity moisture predictor, rather than specific humidity, was needed for downscaled projections to be consistent with direct model output projections.

scholarly journals Technical Note: On the use of nudging for aerosol-climate model intercomparison studies

2014 ◽  
Vol 14 (7) ◽  
pp. 10311-10343 ◽  
Author(s):  
K. Zhang ◽  
H. Wan ◽  
X. Liu ◽  
S. J. Ghan ◽  
G. J. Kooperman ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Cloud Amount ◽  
Temperature Fields ◽  
Specific Humidity ◽  
Radiation Budget ◽  
Anthropogenic Aerosols ◽  
Anthropogenic Aerosol ◽  
The Impact

Abstract. Nudging is an assimilation technique widely used in the development and evaluation of climate models. Constraining the simulated wind and temperature fields using global weather reanalysis facilitates more straightforward comparison between simulation and observation, and reduces uncertainties associated with natural variabilities of the large-scale circulation. On the other hand, the forcing introduced by nudging can be strong enough to change the basic characteristics of the model climate. In the paper we show that for the Community Atmosphere Model version 5, due to the systematic temperature bias in the standard model and the sensitivity of simulated ice formation to anthropogenic aerosol concentration, nudging towards reanalysis results in substantial reductions in the ice cloud amount and the impact of anthropogenic aerosols on longwave cloud forcing. In order to reduce discrepancies between the nudged and unconstrained simulations and meanwhile take the advantages of nudging, two alternative experimentation methods are evaluated. The first one constrains only the horizontal winds. The second method nudges both winds and temperature, but replaces the long-term climatology of the reanalysis by that of the model. Results show that both methods lead to substantially improved agreement with the free-running model in terms of the top-of-atmosphere radiation budget and cloud ice amount. The wind-only nudging is more convenient to apply, and provides higher correlations of the wind fields, geopotential height and specific humidity between simulation and reanalysis. This suggests nudging the horizontal winds but not temperature is a good strategy for the investigation of aerosol indirect effects through ice clouds, since it provides well-constrained meteorology without strongly perturbing the model's mean climate.

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.

scholarly journals The Development and Assessment of a Model-, Grid-, and Basin-Independent Tropical Cyclone Detection Scheme

2013 ◽  
Vol 26 (15) ◽  
pp. 5493-5507 ◽  
Author(s):  
K. J. Tory ◽  
S. S. Chand ◽  
R. A. Dare ◽  
J. L. McBride
Keyword(s):  
Tropical Cyclone ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Vertical Wind Shear ◽  
Lower Troposphere ◽  
Specific Humidity ◽  
Model Data ◽  
Detector Performance ◽  
Detection Scheme

Abstract A novel approach to tropical cyclone (TC) detection in coarse-resolution numerical model data is introduced and assessed. This approach differs from traditional detectors in two main ways. First, it was developed and tuned using 20 yr of ECMWF Interim Re-Analysis (ERA-Interim) data, rather than using climate model data. This ensures that the detector is independent of any climate models to which it will later be applied. Second, only relatively large-scale parameters resolvable in climate models are included, in order to minimize any grid-resolution dependence on parameter thresholds. This approach is taken in an attempt to construct a unified TC detection procedure applicable to all climate models without the need for any further tuning or adjustment. Unlike traditional detectors that seek to identify TCs directly, the authors' method seeks to identify conditions favorable for TC formation. Favorable TC formation regions at the center of closed circulations in the lower troposphere to the midtroposphere are identified using a low-deformation vorticity parameter. Additional relative and specific humidity thresholds are applied to ensure the thermodynamic environment is favorable, and a vertical wind shear threshold is applied to eliminate storms in a destructive shear environment. A further requirement is that thresholds for all parameters must be satisfied for at least 48 h before a TC is deemed to have developed. A thorough assessment of the detector performance is provided. It is demonstrated that the method reproduces realistic TC genesis frequency and spatial distributions in the ERA-Interim data. Application of the detector to four climate models is presented in a companion paper.

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).

scholarly journals Recent trends in climate variability at the local scale using 40 years of observations: the case of the Paris region of France

2019 ◽  
Vol 19 (20) ◽  
pp. 13129-13155 ◽  
Author(s):  
Justine Ringard ◽  
Marjolaine Chiriaco ◽  
Sophie Bastin ◽  
Florence Habets
Keyword(s):  
Global Warming ◽  
Large Scale ◽  
Climate Models ◽  
Specific Humidity ◽  
Maximum Temperature ◽  
Strong Increase ◽  
Large Scale Circulation ◽  
Surface Atmosphere ◽  
Paris Region ◽  
Local Variability

Abstract. For several years, global warming has been unequivocal, leading to climate change at global, regional and local scales. A good understanding of climate characteristics and local variability is important for adaptation and response. Indeed, the contribution of local processes and their understanding in the context of warming are still very little studied and poorly represented in climate models. Improving the knowledge of surface–atmosphere feedback effects at local scales is therefore important for future projections. Using observed data in the Paris region from 1979 to 2017, this study characterizes the changes observed over the last 40 years for six climatic parameters (e.g. mean, maximum and minimum air temperature at 2 m, 2 m relative and specific humidities and precipitation) at the annual and seasonal scales and in summer, regardless of large-scale circulation, with an attribution of which part of the change is linked to large-scale circulation or thermodynamic. The results show that some trends differ from the ones observed at the regional or global scale. Indeed, in the Paris region, the maximum temperature increases faster than does the minimum temperature. The most significant trends are observed in spring and in summer, with a strong increase in temperature and a very strong decrease in relative humidity, while specific humidity and precipitation show no significant trends. The summer trends can be explained more precisely using large-scale circulation, especially regarding the evolution of the precipitation and specific humidity. The analysis indicates the important role of surface–atmosphere feedback in local variability and that this feedback is amplified or inhibited in a context of global warming, especially in an urban environment.

scholarly journals Process-oriented analysis of aircraft soot-cirrus interactions constrains the climate impact of aviation

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Bernd Kärcher ◽  
Fabian Mahrt ◽  
Claudia Marcolli
Keyword(s):  
Radiative Forcing ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Climate Impact ◽  
Global Climate Models ◽  
Soot Particles ◽  
Cloud Ice ◽  
The Impact

AbstractFully accounting for the climate impact of aviation requires a process-level understanding of the impact of aircraft soot particle emissions on the formation of ice clouds. Assessing this impact with the help of global climate models remains elusive and direct observations are lacking. Here we use a high-resolution cirrus column model to investigate how aircraft-emitted soot particles, released after ice crystals sublimate at the end of the lifetime of contrails and contrail cirrus, perturb the formation of cirrus. By allying cloud simulations with a measurement-based description of soot-induced ice formation, we find that only a small fraction (<1%) of the soot particles succeeds in forming cloud ice alongside homogeneous freezing of liquid aerosol droplets. Thus, soot-perturbed and homogeneously-formed cirrus fundamentally do not differ in optical depth. Our results imply that climate model estimates of global radiative forcing from interactions between aircraft soot and large-scale cirrus may be overestimates. The improved scientific understanding reported here provides a process-based underpinning for improved climate model parametrizations and targeted field observations.

Some Pitfalls in Statistical Downscaling of Future Climate

2018 ◽  
Vol 99 (4) ◽  
pp. 791-803 ◽  
Author(s):  
John R. Lanzante ◽  
Keith W. Dixon ◽  
Mary Jo Nath ◽  
Carolyn E. Whitlock ◽  
Dennis Adams-Smith
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Climate Change Impacts ◽  
Future Climate ◽  
Added Value ◽  
Historical Period ◽  
Perfect Model

AbstractStatistical downscaling (SD) is commonly used to provide information for the assessment of climate change impacts. Using as input the output from large-scale dynamical climate models and observation-based data products, SD aims to provide a finer grain of detail and to mitigate systematic biases. It is generally recognized as providing added value. However, one of the key assumptions of SD is that the relationships used to train the method during a historical period are unchanged in the future, in the face of climate change. The validity of this assumption is typically quite difficult to assess in the normal course of analysis, as observations of future climate are lacking. We approach this problem using a “perfect model” experimental design in which high-resolution dynamical climate model output is used as a surrogate for both past and future observations.We find that while SD in general adds considerable value, in certain well-defined circumstances it can produce highly erroneous results. Furthermore, the breakdown of SD in these contexts could not be foreshadowed during the typical course of evaluation based on only available historical data. We diagnose and explain the reasons for these failures in terms of physical, statistical, and methodological causes. These findings highlight the need for caution in the use of statistically downscaled products and the need for further research to consider other hitherto unknown pitfalls, perhaps utilizing more advanced perfect model designs than the one we have employed.

scholarly journals Kinematic and diabatic vertical velocity climatologies from a chemistry climate model

2016 ◽  
Vol 16 (10) ◽  
pp. 6223-6239 ◽  
Author(s):  
Charlotte Marinke Hoppe ◽  
Felix Ploeger ◽  
Paul Konopka ◽  
Rolf Müller
Keyword(s):  
Vertical Velocity ◽  
Atmospheric Chemistry ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Boreal Summer ◽  
Uniform Structure ◽  
Residual Circulation ◽  
Mixing Processes ◽  
The Impact

Abstract. The representation of vertical velocity in chemistry climate models is a key element for the representation of the large-scale Brewer–Dobson circulation in the stratosphere. Here, we diagnose and compare the kinematic and diabatic vertical velocities in the ECHAM/Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model. The calculation of kinematic vertical velocity is based on the continuity equation, whereas diabatic vertical velocity is computed using diabatic heating rates. Annual and monthly zonal mean climatologies of vertical velocity from a 10-year simulation are provided for both kinematic and diabatic vertical velocity representations. In general, both vertical velocity patterns show the main features of the stratospheric circulation, namely, upwelling at low latitudes and downwelling at high latitudes. The main difference in the vertical velocity pattern is a more uniform structure for diabatic and a noisier structure for kinematic vertical velocity. Diabatic vertical velocities show higher absolute values both in the upwelling branch in the inner tropics and in the downwelling regions in the polar vortices. Further, there is a latitudinal shift of the tropical upwelling branch in boreal summer between the two vertical velocity representations with the tropical upwelling region in the diabatic representation shifted southward compared to the kinematic case. Furthermore, we present mean age of air climatologies from two transport schemes in EMAC using these different vertical velocities and analyze the impact of residual circulation and mixing processes on the age of air. The age of air distributions show a hemispheric difference pattern in the stratosphere with younger air in the Southern Hemisphere and older air in the Northern Hemisphere using the transport scheme with diabatic vertical velocities. Further, the age of air climatology from the transport scheme using diabatic vertical velocities shows a younger mean age of air in the inner tropical upwelling branch and an older mean age in the extratropical tropopause region.

scholarly journals Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies

2016 ◽  
Vol 20 (2) ◽  
pp. 685-696 ◽  
Author(s):  
E. P. Maurer ◽  
D. L. Ficklin ◽  
W. Wang
Keyword(s):  
Spatial Scale ◽  
Statistical Downscaling ◽  
Bias Correction ◽  
Large Scale ◽  
Climate Model ◽  
Model Output ◽  
Quantile Mapping ◽  
Impact Studies ◽  
Mapping Bias ◽  
Climate Model Output

Abstract. Statistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in the large-scale signal. A simple and generally effective method for accommodating systematic biases in large-scale model output is quantile mapping, which has been applied to many variables and shown to reduce biases on average, even in the presence of non-stationarity. Quantile-mapping bias correction has been applied at spatial scales ranging from hundreds of kilometers to individual points, such as weather station locations. Since water resources and other models used to simulate climate impacts are sensitive to biases in input meteorology, there is a motivation to apply bias correction at a scale fine enough that the downscaled data closely resemble historically observed data, though past work has identified undesirable consequences to applying quantile mapping at too fine a scale. This study explores the role of the spatial scale at which the quantile-mapping bias correction is applied, in the context of estimating high and low daily streamflows across the western United States. We vary the spatial scale at which quantile-mapping bias correction is performed from 2° ( ∼  200 km) to 1∕8° ( ∼  12 km) within a statistical downscaling procedure, and use the downscaled daily precipitation and temperature to drive a hydrology model. We find that little additional benefit is obtained, and some skill is degraded, when using quantile mapping at scales finer than approximately 0.5° ( ∼  50 km). This can provide guidance to those applying the quantile-mapping bias correction method for hydrologic impacts analysis.

scholarly journals Summer Season Squall-Line Simulations: Sensitivity of Gravity Waves to Physics Parameterization and Implications for Their Parameterization in Global Climate Models

2014 ◽  
Vol 71 (9) ◽  
pp. 3376-3391 ◽  
Author(s):  
Claudia Stephan ◽  
M. Joan Alexander
Keyword(s):  
Gravity Waves ◽  
Momentum Flux ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Middle Atmosphere ◽  
Global Climate ◽  
Global Climate Models ◽  
Squall Line ◽  
The Impact

Abstract Gravity waves have important effects on the middle atmosphere circulation, and those generated by convection are prevalent in the tropics and summer midlatitudes. Numerous case studies have been carried out to investigate their characteristics in high-resolution simulations. Here, the impact of the choice of physics parameterizations on the generation and spectral properties of these waves in models is investigated. Using the Weather Research and Forecasting Model (WRF) a summertime squall line over the Great Plains is simulated in a three-dimensional, nonlinear, and nonhydrostatic mesoscale framework. The distributions of precipitation strength and echo tops in the simulations are compared with radar data. Unsurprisingly, those storm features are most sensitive to the microphysics scheme. However, it is found that these variations in storm morphology have little influence on the simulated stratospheric momentum flux spectra. These results support the fundamental idea behind climate model parameterizations: that the large-scale storm conditions can be used to predict the spectrum of gravity wave momentum flux above the storm irrespective of the convective details that coarse-resolution models cannot capture. The simulated spectra are then contrasted with those obtained from a parameterization used in global climate models. The parameterization reproduces the shape of the spectra reasonably well but their magnitudes remain highly sensitive to the peak heating rate within the convective cells.

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