scholarly journals Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models

2010 ◽  
Vol 44 (2-3) ◽  
pp. 135-150 ◽  
Author(s):  
E Kjellström ◽  
F Boberg ◽  
M Castro ◽  
JH Christensen ◽  
G Nikulin ◽  
...  
Keyword(s):  
Performance Indicators ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Monthly Temperature ◽  
Temperature And Precipitation

scholarly journals Impacts of climate change on temperature, precipitation and hydrology in Finland – studies using bias corrected Regional Climate Model data

2015 ◽  
Vol 12 (3) ◽  
pp. 2657-2706 ◽  
Author(s):  
T. Olsson ◽  
J. Jakkila ◽  
N. Veijalainen ◽  
L. Backman ◽  
J. Kaurola ◽  
...  
Keyword(s):  
Climate Change ◽  
Seasonal Changes ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Control Period ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Catchment Scale ◽  
Temperature And Precipitation

Abstract. Assessment of climate change impacts on climate and hydrology on catchment scale requires reliable information about the average values and climate fluctuations of the past, present and future. Regional Climate Models (RCMs) used in impact studies often produce biased time series of meteorological variables. In this study bias correction of RCM temperature and precipitation for Finland is carried out using different versions of distribution based scaling (DBS) method. The DBS adjusted RCM data is used as input of a hydrological model to simulate changes in discharges in four study catchments in different parts of Finland. The annual mean discharges and seasonal variation simulated with the DBS adjusted temperature and precipitation data are sufficiently close to observed discharges in the control period (1961–2000) and produce more realistic projections for mean annual and seasonal changes in discharges than the uncorrected RCM data. Furthermore, with most scenarios the DBS method used preserves the temperature and precipitation trends of the uncorrected RCM data during 1961–2100. However, if the biases in the mean or the SD of the uncorrected temperatures are large, significant biases after DBS adjustment may remain or temperature trends may change, increasing the uncertainty of climate change projections. The DBS method influences especially the projected seasonal changes in discharges and the use of uncorrected data can produce unrealistic seasonal discharges and changes. The projected changes in annual mean discharges are moderate or small, but seasonal distribution of discharges will change significantly.

scholarly journals SA-OBS: A Daily Gridded Surface Temperature and Precipitation Dataset for Southeast Asia

2017 ◽  
Vol 30 (14) ◽  
pp. 5151-5165 ◽  
Author(s):  
Else J. M. van den Besselaar ◽  
Gerard van der Schrier ◽  
Richard C. Cornes ◽  
Aris Suwondo Iqbal ◽  
Albert M. G. Klein Tank
Keyword(s):  
Southeast Asia ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Maximum Temperature ◽  
Climate Assessment ◽  
Temperature And Precipitation ◽  
Interpolation Technique ◽  
Precipitation Events ◽  
Best Estimates

This study introduces a new daily high-resolution land-only observational gridded dataset, called SA-OBS, for precipitation and minimum, mean, and maximum temperature covering Southeast Asia. This dataset improves upon existing observational products in terms of the number of contributing stations, in the use of an interpolation technique appropriate for daily climate observations, and in making estimates of the uncertainty of the gridded data. The dataset is delivered on a 0.25° × 0.25° and a 0.5° × 0.5° regular latitude–longitude grid for the period 1981–2014. The dataset aims to provide best estimates of grid square averages rather than point values to enable direct comparisons with regional climate models. Next to the best estimates, daily uncertainties are quantified. The underlying daily station time series are collected in cooperation between meteorological services in the region: the Southeast Asian Climate Assessment and Dataset (SACA&D). Comparisons are made with station observations and other gridded station or satellite-based datasets (APHRODITE, CMORPH, TRMM). The comparisons show that vast differences exist in the average daily precipitation, the number of rainy days, and the average precipitation on a wet day between these datasets. SA-OBS closely resembles the station observations in terms of dry/wet frequency, the timing of precipitation events, and the reproduction of extreme precipitation. New versions of SA-OBS will be released when the station network in SACA&D has grown further.

Temperature and precipitation verification over Pannonian Basin in EURO-CORDEX simulations during summer season

2020 ◽  
Author(s):  
Irida Lazic ◽  
Vladimir Djurdjevic
Keyword(s):  
Climate Models ◽  
Pannonian Basin ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Summer Season ◽  
Data Set ◽  
Sea Level Pressure ◽  
Temperature And Precipitation ◽  
Time Period ◽  
Precipitation Bias

<p>In previous studies, it was noticed that many Regional Climate Models (RCMs) tend to overestimate mean near surface air temperature and underestimate precipitation in the Pannonian Basin during summer, leading to so-called summer drying problem [1]. Our intention for this study was to analyze temperature and precipitation biases in the state of the art EURO-CORDEX multi-model ensemble results in the summer season. Models’ results from the historical runs, and over time period 1971-2000, for temperature, precipitation and sea level pressure were verified against gridded E-OBS data set. In total there were 30 selected integrations, with different combinations of RCMs and Global Climate Models (GCMs). In order to assess the impact of the different lateral boundary conditions on the results from RCMs simulations, emphasizing the errors of the corresponding driving models used in 30 RCMs simulations, results from driving GCMs are also verified.</p><p>Verification results for selected time period was expressed in term of four verification scores: bias, root mean square error (RMSE), spatial correlation coefficient and standard deviations. Verification scores were evaluated within a sub-domain in the center of the region bounded by longitudes, 14E and 27E, and latitudes, 43.5N and 50N, in which topography elevation is below 200 m. This sub-domain was selected to eliminate the influence of results over the surrounding mountains on spatially averaged scores [2], because previous studies indicated a pronounced summer drying problem in low lying areas. Our analysis showed that 17 RCMs tend to overestimate the temperature, 8 RCMs tend to underestimate the temperature and 5 RCMs tend to estimate temperature around E-OBS gridded data set. On the other hand, most of the RCMs that overestimate the temperature, underestimate the precipitation. According to the results, temperature bias was in the range from -1.9°C to +4.4°C , while precipitation bias was in the range from 42% to -70%. For some models the positive temperature and negative precipitation bias were even more pronounced, leading to the conclusion, that the problem is still present in the majority of analyzed simulations. Analysis of the sea level pressure was conducted as an indirect indicator of errors in advection processes in RCMs, which was indicated, beside others, as a potential precursor of temperature and precipitation biases [3]. To better understand the sources and reasons for summer drying problem further research is needed.</p><p>[1] Kotlarski S. et al., (2014): Regional climate modelling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geoscientific Model Development 7:1297–1333, doi: 10.5194/gmd-7-1297-2014</p><p>[2] Lazic I., Djurdjevic V., (2019): EURO-CORDEX regional climate models’ performances in representing temperature and precipitation over Pannonian Basin, Book of abstracts, 5th PannEx Workshop, 3-5 June 2019, Novi Sad, Serbia.</p><p>[3] Szépszó G., (2006): Adaptation of the REMO model at the Hungarian Meteorological Service (in Hungarian). Proceedings of the 31st Scientific Days for Meteorology, 125–135.</p><p><em>Keywords</em>: summer drying problem, verification, EURO-CORDEX, Pannonian Basin</p><p>Acknowledgement: This study was supported by the Serbian Ministry of Science and Education, under grant no. 176013.</p>

scholarly journals A Maieutic Exploration of Nudging Strategies for Regional Climate Applications Using the WRF Model

2018 ◽  
Vol 57 (8) ◽  
pp. 1883-1906 ◽  
Author(s):  
Tanya L. Spero ◽  
Christopher G. Nolte ◽  
Megan S. Mallard ◽  
Jared H. Bowden
Keyword(s):  
United States ◽  
Climate Models ◽  
Weather Prediction ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Models ◽  
The United States ◽  
Spectral Nudging ◽  
Temperature And Precipitation ◽  
Quality Health

AbstractThe use of nudging in the Weather Research and Forecasting (WRF) Model to constrain regional climate downscaling simulations is gaining in popularity because it can reduce error and improve consistency with the driving data. While some attention has been paid to whether nudging is beneficial for downscaling, very little research has been performed to determine best practices. In fact, many published papers use the default nudging configuration (which was designed for numerical weather prediction), follow practices used by colleagues, or adapt methods developed for other regional climate models. Here, a suite of 45 three-year simulations is conducted with WRF over the continental United States to systematically and comprehensively examine a variety of nudging strategies. The simulations here use a longer test period than did previously published works to better evaluate the robustness of each strategy through all four seasons, through multiple years, and across nine regions of the United States. The analysis focuses on the evaluation of 2-m temperature and precipitation, which are two of the most commonly required downscaled output fields for air quality, health, and ecosystems applications. Several specific recommendations are provided to effectively use nudging in WRF for regional climate applications. In particular, spectral nudging is preferred over analysis nudging. Spectral nudging performs best in WRF when it is used toward wind above the planetary boundary layer (through the stratosphere) and temperature and moisture only within the free troposphere. Furthermore, the nudging toward moisture is very sensitive to the nudging coefficient, and the default nudging coefficient in WRF is too high to be used effectively for moisture.

scholarly journals Evaluating Snow in EURO-CORDEX Regional Climate Models with Observations for the European Alps: Biases and Their Relationship to Orography, Temperature, and Precipitation Mismatches

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 46
Author(s):  
Michael Matiu ◽  
Marcello Petitta ◽  
Claudia Notarnicola ◽  
Marc Zebisch
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Winter Period ◽  
European Alps ◽  
Temperature And Precipitation

Climate models are important tools to assess current and future climate. While they have been extensively used for studying temperature and precipitation, only recently regional climate models (RCMs) arrived at horizontal resolutions that allow studies of snow in complex mountain terrain. Here, we present an evaluation of the snow variables in the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) RCMs with gridded observations of snow cover (from MODIS remote sensing) and temperature and precipitation (E-OBS), as well as with point (station) observations of snow depth and temperature for the European Alps. Large scale snow cover dynamics were reproduced well with some over- and under-estimations depending on month and RCM. The orography, temperature, and precipitation mismatches could on average explain 31% of the variability in snow cover bias across grid-cells, and even more than 50% in the winter period November–April. Biases in average monthly snow depth were remarkably low for reanalysis driven RCMs (<approx. 30 cm), and large for the GCM driven ones (up to 200 cm), when averaged over all stations within 400 m of altitude difference with RCM orography. Some RCMs indicated low snow cover biases and at the same time high snow depth biases, and vice versa. In summary, RCMs showed good skills in reproducing alpine snow cover conditions with regard to their limited horizontal resolution. Detected shortcomings in the models depended on the considered snow variable, season and individual RCM.

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