scholarly journals A climatology of tropical wind shear produced by clustering wind profiles from a climate model

2021 ◽  
Author(s):  
Mark R. Muetzelfedt ◽  
Robert S. Plant ◽  
Peter A. Clark ◽  
Alison J. Stirling ◽  
Steven J. Woolnough
Keyword(s):  
Principal Components ◽  
Wind Shear ◽  
Clustering Algorithm ◽  
Climate Model ◽  
Convective Available Potential Energy ◽  
Model Output ◽  
Tropical Ocean ◽  
Toga Coare ◽  
Climate Model Output ◽  
Wind Profiles

Abstract. A procedure for producing a climatology of tropical wind shear from climate-model output is presented. The procedure is designed to find grid columns in the model where the organization of convection may be present. The climate-model output consists of east–west and north–south wind profiles at 20 equally spaced pressure levels from 1000 hPa to 50 hPa, and the Convective Available Potential Energy (CAPE) as diagnosed by the model’s Convection Parametrization Scheme (CPS). The procedure begins by filtering the wind profiles based on their maximum shear, and on a CAPE threshold of 100 J kg−1. The filtered profiles are normalized using the maximum wind speed at each pressure level, and rotated to align the wind at 850 hPa. From each of the filtered profiles, a sample has been produced with 40 dimensions (20 for each wind direction). The number of dimensions is reduced by using Principal Component Analysis (PCA), where the requirement is that 90 % of the variance must be explained by the principal components. This requires keeping the first seven leading principal components. The samples, as represented by their principal components, can then be clustered using the K-Means Clustering Algorithm (KMCA). 10 clusters are chosen to represent the samples, and the median of each cluster defines a Representative Wind Profile (RWP) – a profile that represents the shear conditions of the wind profiles produced by the climate model. The RWPs are analysed, first in terms of their vertical structure, and then in terms of their geographical and temporal distributions. We find that the RWPs have some features often associated with the organization of convection, such as low-level and mid-level shear. Some of the RWPs can be matched with wind profiles taken from case studies of organization of convection, such as squall lines seen in Tropical Ocean Global Atmosphere, Coupled Atmosphere Ocean Research Experiment (TOGA–COARE). The RWPs’ geographical distributions show that each RWP occurs preferentially in certain regions. Six of the RWPs occur preferentially over land, while three occur preferentially over oceans. The temporal distribution of RWPs shows that they occur preferentially at certain times of the year, with the distributions having mainly one or two modes. Their geographical and temporal distributions are compared with those seen in previous studies of organized convection, and some broad and specific similarities are noted. By performing the analysis on climate-model output, we lay the foundations for the development of the representation of shear-induced organization in a CPS. This would use the same methodology to diagnose where the organization of convection occurs, and modify the CPS in an appropriate manner to represent it.

scholarly journals A climatology of tropical wind shear produced by clustering wind profiles from the Met Office Unified Model (GA7.0)

2021 ◽  
Vol 14 (6) ◽  
pp. 4035-4049
Author(s):  
Mark R. Muetzelfeldt ◽  
Robert S. Plant ◽  
Peter A. Clark ◽  
Alison J. Stirling ◽  
Steven J. Woolnough
Keyword(s):  
Wind Shear ◽  
Clustering Algorithm ◽  
Climate Models ◽  
Climate Model ◽  
Deep Convection ◽  
Convective Available Potential Energy ◽  
Principal Component ◽  
Unified Model ◽  
Wind Profiles ◽  
Organized Convection

Abstract. Toward the goal of linking wind shear with the mesoscale organization of deep convection, a procedure for producing a climatology of tropical wind shear from the output of the Met Office Unified Model climate model is presented. Statistical information from wind profiles from tropical grid columns is used to produce a tractable number (10) of profiles that efficiently span the space of all wind profiles. Physical arguments are used to filter wind profiles that are likely to be associated with organized convection: only grid columns with substantial convective available potential energy (CAPE) and those with shear in the upper quartile are considered. The profiles are rotated so that their wind vectors at 850 hPa are aligned, in order to be able to group like profiles together, and their magnitudes at each level are normalized. To emphasize the effect of lower levels, where the organization effects of shear are thought to be strongest, the profiles above 500 hPa are multiplied by 14. Principal component analysis is used to truncate the number of dimensions of the profiles to seven (which explains 90 % of the variance), and the truncated profiles are clustered using a K-means clustering algorithm. The median of each cluster defines a representative wind profile (RWP). Each cluster contains information from thousands of wind profiles with different locations, times and 850 hPa wind directions. To summarize the clusters statistically, we interpret the RWPs as pseudo-wind profiles and display the geographic frequency, seasonal frequency and histograms of wind direction at 850 hPa for each cluster. Geographic patterns are evident, and certain features of the spatio-temporal distributions are matched to observed distributions of convective organization. The form of the RWPs is also matched to specific wind profiles from case studies of organized convection. By performing the analysis on climate-model output, we lay the foundations for the development of the representation of shear-induced organization in a convection parametrization scheme (CPS). This would use the same methodology to diagnose where the organization of convection occurs and modify the CPS in an appropriate manner to represent it. The procedure could also be used as a diagnostic tool for evaluating and comparing climate models.

Continuous prediction of clay-bed stream erosion in response to climate model output for a small urban watershed

2018 ◽  
Vol 32 (8) ◽  
pp. 1104-1119 ◽  
Author(s):  
Colin P. Brennan ◽  
Parna Parsapour-Moghaddam ◽  
Colin D. Rennie ◽  
Ousmane Seidou
Keyword(s):  
Climate Model ◽  
Model Output ◽  
Urban Watershed ◽  
Climate Model Output ◽  
Stream Erosion

scholarly journals Constructing High-Resolution, Bias-Corrected Climate Products: A Comparison of Methods

2021 ◽  
Vol 60 (4) ◽  
pp. 455-475
Author(s):  
Maike F. Holthuijzen ◽  
Brian Beckage ◽  
Patrick J. Clemins ◽  
Dave Higdon ◽  
Jonathan M. Winter
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Climate Model ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Model Output ◽  
Quantile Mapping ◽  
Station Data ◽  
Climate Model Output ◽  
Mapping Techniques

AbstractHigh-resolution, bias-corrected climate data are necessary for climate impact studies at local scales. Gridded historical data are convenient for bias correction but may contain biases resulting from interpolation. Long-term, quality-controlled station data are generally superior climatological measurements, but because the distribution of climate stations is irregular, station data are challenging to incorporate into downscaling and bias-correction approaches. Here, we compared six novel methods for constructing full-coverage, high-resolution, bias-corrected climate products using daily maximum temperature simulations from a regional climate model (RCM). Only station data were used for bias correction. We quantified performance of the six methods with the root-mean-square-error (RMSE) and Perkins skill score (PSS) and used two ANOVA models to analyze how performance varied among methods. We validated the six methods using two calibration periods of observed data (1980–89 and 1980–2014) and two testing sets of RCM data (1990–2014 and 1980–2014). RMSE for all methods varied throughout the year and was larger in cold months, whereas PSS was more consistent. Quantile-mapping bias-correction techniques substantially improved PSS, while simple linear transfer functions performed best in improving RMSE. For the 1980–89 calibration period, simple quantile-mapping techniques outperformed empirical quantile mapping (EQM) in improving PSS. When calibration and testing time periods were equivalent, EQM resulted in the largest improvements in PSS. No one method performed best in both RMSE and PSS. Our results indicate that simple quantile-mapping techniques are less prone to overfitting than EQM and are suitable for processing future climate model output, whereas EQM is ideal for bias correcting historical climate model output.

scholarly journals Deep Learning for Climate Model Output Statistics

2021 ◽  
Author(s):  
Michael Steininger ◽  
Daniel Abel ◽  
Katrin Ziegler ◽  
Anna Krause ◽  
Heiko Paeth ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Deep Learning ◽  
Climate Models ◽  
Climate Model ◽  
Model Output ◽  
Climate Change Effects ◽  
Climate Model Output ◽  
Improved Performance ◽  
Model Output Statistics

<p>Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.</p>

scholarly journals Climate Model Output Rewriter (CMOR)

2004 ◽  
Author(s):  
K Taylor ◽  
C Doutriaux ◽  
J Peterschmitt
Keyword(s):  
Climate Model ◽  
Model Output ◽  
Climate Model Output

scholarly journals Does Quantile Mapping of Simulated Precipitation Correct for Biases in Transition Probabilities and Spell Lengths?

2016 ◽  
Vol 29 (5) ◽  
pp. 1605-1615 ◽  
Author(s):  
Jan Rajczak ◽  
Sven Kotlarski ◽  
Christoph Schär
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Transition Probabilities ◽  
Weather Generator ◽  
Mapping Technique ◽  
Model Output ◽  
Quantile Mapping ◽  
Stochastic Weather Generator ◽  
Simulated Precipitation ◽  
Climate Model Output

Abstract Climate impact studies constitute the basis for the formulation of adaptation strategies. Usually such assessments apply statistically postprocessed output of climate model projections to force impact models. Increasingly, time series with daily resolution are used, which require high consistency, for instance with respect to transition probabilities (TPs) between wet and dry days and spell durations. However, both climate models and commonly applied statistical tools have considerable uncertainties and drawbacks. This paper compares the ability of 1) raw regional climate model (RCM) output, 2) bias-corrected RCM output, and 3) a conventional weather generator (WG) that has been calibrated to match observed TPs to simulate the sequence of dry, wet, and very wet days at a set of long-term weather stations across Switzerland. The study finds systematic biases in TPs and spell lengths for raw RCM output, but a substantial improvement after bias correction using the deterministic quantile mapping technique. For the region considered, bias-corrected climate model output agrees well with observations in terms of TPs as well as dry and wet spell durations. For the majority of cases (models and stations) bias-corrected climate model output is similar in skill to a simple Markov chain stochastic weather generator. There is strong evidence that bias-corrected climate model simulations capture the atmospheric event sequence more realistically than a simple WG.

scholarly journals How Much Should Climate Model Output Be Smoothed in Space?

2011 ◽  
Vol 24 (3) ◽  
pp. 867-880 ◽  
Author(s):  
Jouni Räisänen ◽  
Jussi S. Ylhäisi
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Model ◽  
Future Climate Change ◽  
Small Scale ◽  
Model Output ◽  
Climate Change Projections ◽  
Local Conditions ◽  
Climate Model Output ◽  
Mean Climate

Abstract The general decrease in the quality of climate model output with decreasing scale suggests a need for spatial smoothing to suppress the most unreliable small-scale features. However, even if correctly simulated, a large-scale average retained by the smoothing may not be representative of the local conditions, which are of primary interest in many impact studies. Here, the authors study this trade-off using simulations of temperature and precipitation by 24 climate models within the Third Coupled Model Intercomparison Project, to find the scale of smoothing at which the mean-square difference between smoothed model output and gridbox-scale reality is minimized. This is done for present-day time mean climate, recent temperature trends, and projections of future climate change, using cross validation between the models for the latter. The optimal scale depends strongly on the number of models used, being much smaller for multimodel means than for individual model simulations. It also depends on the variable considered and, in the case of climate change projections, the time horizon. For multimodel-mean climate change projections for the late twenty-first century, only very slight smoothing appears to be beneficial, and the resulting potential improvement is negligible for practical purposes. The use of smoothing as a means to improve the sampling for probabilistic climate change projections is also briefly explored.

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