A weighting proposal for an ensemble of regional climate models over Europe driven by 1961-2000 ERA40 based on monthly precipitation probability density functions

2009 ◽  
pp. n/a-n/a ◽  
Author(s):  
E. Sánchez ◽  
R. Romera ◽  
M. A. Gaertner ◽  
C. Gallardo ◽  
M. Castro
Keyword(s):  
Probability Density ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Probability Density Functions ◽  
Density Functions ◽  
Monthly Precipitation

scholarly journals Cool season precipitation projections for California and the Western United States in NA-CORDEX models

2021 ◽  
Author(s):  
Kelly Mahoney ◽  
James D. Scott ◽  
Michael Alexander ◽  
Rachel McCrary ◽  
Mimi Hughes ◽  
...  
Keyword(s):  
United States ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Western United States ◽  
Monthly Precipitation ◽  
Wet Season ◽  
Cool Season ◽  
Projected Change

AbstractUnderstanding future precipitation changes is critical for water supply and flood risk applications in the western United States. The North American COordinated Regional Downscaling EXperiment (NA-CORDEX) matrix of global and regional climate models at multiple resolutions (~ 50-km and 25-km grid spacings) is used to evaluate mean monthly precipitation, extreme daily precipitation, and snow water equivalent (SWE) over the western United States, with a sub-regional focus on California. Results indicate significant model spread in mean monthly precipitation in several key water-sensitive areas in both historical and future projections, but suggest model agreement on increasing daily extreme precipitation magnitudes, decreasing seasonal snowpack, and a shortening of the wet season in California in particular. While the beginning and end of the California cool season are projected to dry according to most models, the core of the cool season (December, January, February) shows an overall wetter projected change pattern. Daily cool-season precipitation extremes generally increase for most models, particularly in California in the mid-winter months. Finally, a marked projected decrease in future seasonal SWE is found across all models, accompanied by earlier dates of maximum seasonal SWE, and thus a shortening of the period of snow cover as well. Results are discussed in the context of how the diverse model membership and variable resolutions offered by the NA-CORDEX ensemble can be best leveraged by stakeholders faced with future water planning challenges.

scholarly journals A climatology of polar stratospheric cloud composition between 2002 and 2012 based on MIPAS/Envisat observations

2018 ◽  
Vol 18 (7) ◽  
pp. 5089-5113 ◽  
Author(s):  
Reinhold Spang ◽  
Lars Hoffmann ◽  
Rolf Müller ◽  
Jens-Uwe Grooß ◽  
Ines Tritscher ◽  
...  
Keyword(s):  
Probability Density ◽  
Climate Models ◽  
Michelson Interferometer ◽  
European Space Agency ◽  
Equilibrium Temperature ◽  
Probability Density Functions ◽  
Solid Particles ◽  
Ratio Method ◽  
Orthogonal Polarization ◽  
Density Functions

Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument aboard the European Space Agency (ESA) Envisat satellite operated from July 2002 to April 2012. The infrared limb emission measurements provide a unique dataset of day and night observations of polar stratospheric clouds (PSCs) up to both poles. A recent classification method for PSC types in infrared (IR) limb spectra using spectral measurements in different atmospheric window regions has been applied to the complete mission period of MIPAS. The method uses a simple probabilistic classifier based on Bayes' theorem with a strong independence assumption on a combination of a well-established two-colour ratio method and multiple 2-D probability density functions of brightness temperature differences. The Bayesian classifier distinguishes between solid particles of ice, nitric acid trihydrate (NAT), and liquid droplets of supercooled ternary solution (STS), as well as mixed types. A climatology of MIPAS PSC occurrence and specific PSC classes has been compiled. Comparisons with results from the classification scheme of the spaceborne lidar Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol-Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite show excellent correspondence in the spatial and temporal evolution for the area of PSC coverage (APSC) even for each PSC class. Probability density functions of the PSC temperature, retrieved for each class with respect to equilibrium temperature of ice and based on coincident temperatures from meteorological reanalyses, are in accordance with the microphysical knowledge of the formation processes with respect to temperature for all three PSC types. This paper represents unprecedented pole-covering day- and nighttime climatology of the PSC distributions and their composition of different particle types. The dataset allows analyses on the temporal and spatial development of the PSC formation process over multiple winters. At first view, a more general comparison of APSC and AICE retrieved from the observations and from the existence temperature for NAT and ice particles based on the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis temperature data shows the high potential of the climatology for the validation and improvement of PSC schemes in chemical transport and chemistry–climate models.

Characterizing Predictability of Precipitation Means and Extremes over the Conterminous United States, 1949–2010*

2016 ◽  
Vol 29 (7) ◽  
pp. 2621-2633 ◽  
Author(s):  
Mingkai Jiang ◽  
Benjamin S. Felzer ◽  
Dork Sahagian
Keyword(s):  
United States ◽  
Pacific Northwest ◽  
West Coast ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
The United States ◽  
Monthly Precipitation ◽  
The West ◽  
Precipitation Seasonality

Abstract The proper understanding of precipitation variability, seasonality, and predictability are important for effective environmental management. Precipitation and its associated extremes vary in magnitude and duration both spatially and temporally, making it one of the most challenging climate parameters to predict on the basis of global and regional climate models. Using information theory, an improved understanding of precipitation predictability in the conterminous United States over the period of 1949–2010 is sought based on a gridded monthly precipitation dataset. Predictability is defined as the recurrent likelihood of patterns described by the metrics of magnitude variability and seasonality. It is shown that monthly mean precipitation and duration-based dry and wet extremes are generally highly variable in the east compared to those in the west, while the reversed spatial pattern is observed for intensity-based wetness indices except along the Pacific Northwest coast. It is thus inferred that, over much of the U.S. landscape, variations of monthly mean precipitation are driven by the variations in precipitation occurrences rather than the intensity of infrequent heavy rainfall. It is further demonstrated that precipitation seasonality for means and extremes is homogeneously invariant within the United States, with the exceptions of the West Coast, Florida, and parts of the Midwest, where stronger seasonality is identified. A proportionally higher role of variability in regulating precipitation predictability is demonstrated. Seasonality surpasses variability only in parts of the West Coast. The quantified patterns of predictability for precipitation means and extremes have direct applications to those phenomena influenced by climate periodicity, such as biodiversity and ecosystem management.

Empirical Downscaling of High-Resolution Regional Precipitation from Large-Scale Reanalysis Fields

2012 ◽  
Vol 51 (1) ◽  
pp. 100-114 ◽  
Author(s):  
Robert E. Nicholas ◽  
David S. Battisti
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Jiangxi Province ◽  
Monthly Precipitation ◽  
Upper Colorado River Basin ◽  
Precipitation Anomalies ◽  
Empirical Downscaling ◽  
High Skill

AbstractThis study describes an EOF-based technique for statistical downscaling of high-spatial-resolution monthly-mean precipitation from large-scale reanalysis circulation fields. The method is demonstrated and evaluated for four widely separated locations: the southeastern United States, the upper Colorado River basin, China’s Jiangxi Province, and central Europe. For each location, the EOF-based downscaling models successfully reproduce the observed annual cycle while eliminating the biases seen in NCEP–NCAR reanalysis precipitation. They also frequently reproduce the monthly precipitation anomalies with greater fidelity than is seen in the precipitation field derived directly from reanalysis, and they outperform a suite of regional climate models over the two U.S. locations. With the relatively high skill achieved over a range of climate regimes, this technique may be a viable alternative to numerical downscaling of monthly-mean precipitation for many locations.

Regional Extreme Monthly Precipitation Simulated by NARCCAP RCMs

2010 ◽  
Vol 11 (6) ◽  
pp. 1373-1379 ◽  
Author(s):  
William J. Gutowski ◽  
Raymond W. Arritt ◽  
Sho Kawazoe ◽  
David M. Flory ◽  
Eugene S. Takle ◽  
...  
Keyword(s):  
Mississippi River ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Regional Climate Models ◽  
Department Of Energy ◽  
Monthly Precipitation ◽  
The North ◽  
Climate Change Assessment ◽  
Environmental Prediction

Abstract This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10% of months during the cold half of the year (October–March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations.

scholarly journals Evaluation of the AR4 Climate Models’ Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions

2007 ◽  
Vol 20 (17) ◽  
pp. 4356-4376 ◽  
Author(s):  
S. E. Perkins ◽  
A. J. Pitman ◽  
N. J. Holbrook ◽  
J. McAneney
Keyword(s):  
Probability Density ◽  
Minimum Temperature ◽  
Climate Models ◽  
Quantitative Measure ◽  
Probability Density Functions ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Density Functions ◽  
Coupled Climate Models

Abstract The coupled climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change are evaluated. The evaluation is focused on 12 regions of Australia for the daily simulation of precipitation, minimum temperature, and maximum temperature. The evaluation is based on probability density functions and a simple quantitative measure of how well each climate model can capture the observed probability density functions for each variable and each region is introduced. Across all three variables, the coupled climate models perform better than expected. Precipitation is simulated reasonably by most and very well by a small number of models, although the problem with excessive drizzle is apparent in most models. Averaged over Australia, 3 of the 14 climate models capture more than 80% of the observed probability density functions for precipitation. Minimum temperature is simulated well, with 10 of the 13 climate models capturing more than 80% of the observed probability density functions. Maximum temperature is also reasonably simulated with 6 of 10 climate models capturing more than 80% of the observed probability density functions. An overall ranking of the climate models, for each of precipitation, maximum, and minimum temperatures, and averaged over these three variables, is presented. Those climate models that are skillful over Australia are identified, providing guidance on those climate models that should be used in impacts assessments where those impacts are based on precipitation or temperature. These results have no bearing on how well these models work elsewhere, but the methodology is potentially useful in assessing which of the many climate models should be used by impacts groups.

scholarly journals Ensemble Precipitation Estimation Using a Fuzzy Rule-Based Model

2021 ◽  
Vol 5 (1) ◽  
pp. 48
Author(s):  
O. Burak Akgun ◽  
Elcin Kentel
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Climate Models ◽  
Regional Climate ◽  
Fuzzy Rule ◽  
Regional Climate Models ◽  
Subtractive Clustering ◽  
Monthly Precipitation ◽  
Precipitation Time Series ◽  
Rule Based

In this study, a Takagi-Sugeno (TS) fuzzy rule-based (FRB) model is used for ensembling precipitation time series. The TS FRB model takes precipitation predictions of grid-based regional climate models (RCMs) from the EUR11 domain, available from the CORDEX database, as inputs to generate ensembled precipitation time series for two meteorological stations (MSs) in the Mediterranean region of Turkey. For each MS, RCM data that are available at the closest grid to the corresponding MSs are used. To generate the fuzzy rules of the TS FRB model, the subtractive clustering algorithm (SC) is utilized. Together with the TS FRB, the simple ensemble mean approach is also applied, and the performances of these two model results and individual RCM predictions are compared. The results show that ensembled models outperform individual RCMs, for monthly precipitation, for both MSs. On the other hand, although ensemble models capture the general trend in the observations, they underestimate the peak precipitation events.

scholarly journals Multi-Criteria Decision Framework to Evaluate Bias Corrected Climate Change Projections in the Piracicaba River Basin

2021 ◽  
Author(s):  
Camila Billerbeck ◽  
Ligia Monteiro da Silva ◽  
Silvana Susko Marcellini ◽  
Arisvaldo Méllo Junior
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Annual Rainfall ◽  
Monthly Precipitation ◽  
Climate Change Effects ◽  
Piracicaba River

Abstract Regional climate models (RCM) are the main tools for climate change impacts assessment in hydrological studies. These models, however, often show biases when compared to historical observations. Bias Correction (BC) are useful techniques to improve climate projection outputs. This study presents a multi-criteria decision analysis (MCDA) framework to compare combinations of RCM with selected BC methods. The comparison was based on the modified Kling-Gupta efficiency (KGE’). The criteria evaluated the general capability of models in reproducing the observed data main statistics. Other criteria evaluated were the relevant aspects for hydrological studies, such as seasonality, dry and wet periods. We applied four BC methods in four RCM monthly rainfall outputs from 1961 to 2005 in the Piracicaba river basin. The Linear Scaling (LS) method showed higher improvements in the general performance of the models. The RCM Eta-HadGEM2-ES, corrected with Standardized Reconstruction (SdRc) method, achieved the best results when compared to the observed precipitation. The bias corrected projected monthly precipitation (2006-2098) preserved the main signal of climate change effects when compared to the original outputs regarding annual rainfall. However, SdRc produced significant decrease in monthly average rainfall, higher than 45% for July, August and September for RCP4.5 and RCP8.5 scenarios.

scholarly journals Correcting mean and extremes in monthly precipitation from 8 regional climate models over Europe

2012 ◽  
Vol 8 (2) ◽  
pp. 953-986 ◽  
Author(s):  
B. Kurnik ◽  
L. Kajfež-Bogataj ◽  
A. Ceglar
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Skill Score ◽  
Brier Score ◽  
Monthly Precipitation ◽  
Brier Skill Score ◽  
European Area ◽  
East End

Abstract. We corrected monthly precipitation from 8 regional climate models using statistical bias correction. All models were corrected according to observations and parameters for bias correction were obtained for all models separately in every grid cells over European domain, using data between 1961 and 1990. Bias correction was validated in the period between 1991 and 2010 with RMSE, Brier score and Brier skill score. The results are encouraging, as mean and extremes were effectively corrected. After applying correction, large biases over Alps, at the East Adriatic cost, west coast of Norway and at the east end of the domain were removed. RMSE of corrected precipitation was lower than RMSE of simulated in 85% of European area and correction for all models failed in only 1.5% of European area. Also extremes were effectively corrected. According to the Brier skill score the probability for dry months was corrected in more than 52% of the European area and heavy precipitation events were corrected in almost 90% of the area. All validation measures suggest the correction of monthly precipitation was successful and therefore we can argue that the corrected precipitation fields will improve results of the climate impact models.

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