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 Water Budgets of Managed Forests in Northeast Germany under Climate Change—Results from a Model Study on Forest Monitoring Sites

2021 ◽  
Vol 11 (5) ◽  
pp. 2403
Author(s):  
Daniel Ziche ◽  
Winfried Riek ◽  
Alexander Russ ◽  
Rainer Hentschel ◽  
Jan Martin
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Regional Climate ◽  
Coupled Model ◽  
Regional Climate Models ◽  
Realistic Model ◽  
Forest Monitoring ◽  
Climate Change Scenarios ◽  
Historical Time ◽  
Time Period

To develop measures to reduce the vulnerability of forests to drought, it is necessary to estimate specific water balances in sites and to estimate their development with climate change scenarios. We quantified the water balance of seven forest monitoring sites in northeast Germany for the historical time period 1961–2019, and for climate change projections for the time period 2010–2100. We used the LWF-BROOK90 hydrological model forced with historical data, and bias-adjusted data from two models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) downscaled with regional climate models under the representative concentration pathways (RCPs) 2.6 and 8.5. Site-specific monitoring data were used to give a realistic model input and to calibrate and validate the model. The results revealed significant trends (evapotranspiration, dry days (actual/potential transpiration < 0.7)) toward drier conditions within the historical time period and demonstrate the extreme conditions of 2018 and 2019. Under RCP8.5, both models simulate an increase in evapotranspiration and dry days. The response of precipitation to climate change is ambiguous, with increasing precipitation with one model. Under RCP2.6, both models do not reveal an increase in drought in 2071–2100 compared to 1990–2019. The current temperature increase fits RCP8.5 simulations, suggesting that this scenario is more realistic than RCP2.6.

scholarly journals Solar Irradiance and Temperature Variability and Projected Trends Analysis in Burundi

Climate ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 83 ◽  
Author(s):  
Agnidé Emmanuel Lawin ◽  
Marc Niyongendako ◽  
Célestin Manirakiza
Keyword(s):  
Solar Irradiance ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Historical Period ◽  
Projection Data ◽  
Data Set ◽  
Variability Analysis ◽  
The Future

This paper assessed the variability and projected trends of solar irradiance and temperature in the East of Burundi. Observed temperature from meteorological stations and the MERRA-2 data set provided by NASA/Goddard Space Flight Center are used over the historical period 1976–2005. In addition, solar irradiance data provided by SoDa database were considered. Furthermore, projection data from eight Regional Climate Models were used over the periods 2026–2045 and 2066–2085. The variability analysis was performed using a standardized index. Projected trends and changes in the future climate were respectively detected through Mann-Kendall and t-tests. The findings over the historical period revealed increase temperature and decrease in solar irradiance over the last decades of the 20th century. At a monthly scale, the variability analysis showed that excesses in solar irradiance coincide with the dry season, which led to the conclusion that it may be a period of high production for solar energy. In the future climate, upward trends in temperature are expected over the two future periods, while no significant trends are forecasted in solar irradiance over the entire studied region. However, slight decreases and significant changes in solar irradiance have been detected over all regions.

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.

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 Regional climate model simulations as input for hydrological applications: evaluation of uncertainties

2005 ◽  
Vol 5 ◽  
pp. 119-125 ◽  
Author(s):  
S. Kotlarski ◽  
A. Block ◽  
U. Böhm ◽  
D. Jacob ◽  
K. Keuler ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Reference Data ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Mean Annual Precipitation ◽  
Data Sets ◽  
Data Set ◽  
Climate Model Simulations ◽  
Hydrological Applications

Abstract. The ERA15 Reanalysis (1979-1993) has been dynamically downscaled over Central Europe using 4 different regional climate models. The regional simulations were analysed with respect to 2m temperature and total precipitation, the main input parameters for hydrological applications. Model results were validated against three reference data sets (ERA15, CRU, DWD) and uncertainty ranges were derived. For mean annual 2 m temperature over Germany, the simulation bias lies between -1.1°C and +0.9°C depending on the combination of model and reference data set. The bias of mean annual precipitation varies between -31 and +108 mm/year. Differences between RCM results are of the same magnitude as differences between the reference data sets.

scholarly journals Towards a definitive historical high-resolution climate dataset for Ireland – promoting climate research in Ireland

2019 ◽  
Vol 15 ◽  
pp. 263-276
Author(s):  
Jason Flanagan ◽  
Paul Nolan ◽  
Ray McGrath ◽  
Christopher Werner
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Absolute Error ◽  
Sea Level Pressure ◽  
Wind Speeds ◽  
Uncertainty Estimates ◽  
Soil Temperatures ◽  
Error Standard Deviation

Abstract. There is strong and constant demand from various sectors (research, industry and government) for long-term, high-resolution (both temporal and spatial), gridded climate datasets. To address this demand, the Irish Centre for High-End Computing (ICHEC) has recently performed two high-resolution simulations of the Irish climate, utilising the Regional Climate Models (RCMs) COSMO-CLM5 and WRF v3.7.1. The datasets produced contain hourly outputs for an array of sub-surface, surface and atmospheric fields for the entire 36-year period 1981–2016. In this work, we list the climate variables that have been archived at ICHEC. We present preliminary uncertainty estimates (error, standard deviation, mean absolute error) based on Met Éireann station observations, for several of the more commonly used variables: 2 m temperature, 10 m wind speeds and mean sea level pressure at the hourly time scale; and precipitation at hourly and daily time scales. Additionally, analyses of 10 cm soil temperatures, CAPE 3 km, Showalter index and surface lifted index are presented.

scholarly journals On the importance of observational data properties when assessing regional climate model performance of extreme precipitation

2013 ◽  
Vol 17 (11) ◽  
pp. 4323-4337 ◽  
Author(s):  
M. A. Sunyer ◽  
H. J. D. Sørup ◽  
O. B. Christensen ◽  
H. Madsen ◽  
D. Rosbjerg ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Observational Data ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Model Performance ◽  
Regional Climate Models ◽  
Data Sets ◽  
Data Set

Abstract. In recent years, there has been an increase in the number of climate studies addressing changes in extreme precipitation. A common step in these studies involves the assessment of the climate model performance. This is often measured by comparing climate model output with observational data. In the majority of such studies the characteristics and uncertainties of the observational data are neglected. This study addresses the influence of using different observational data sets to assess the climate model performance. Four different data sets covering Denmark using different gauge systems and comprising both networks of point measurements and gridded data sets are considered. Additionally, the influence of using different performance indices and metrics is addressed. A set of indices ranging from mean to extreme precipitation properties is calculated for all the data sets. For each of the observational data sets, the regional climate models (RCMs) are ranked according to their performance using two different metrics. These are based on the error in representing the indices and the spatial pattern. In comparison to the mean, extreme precipitation indices are highly dependent on the spatial resolution of the observations. The spatial pattern also shows differences between the observational data sets. These differences have a clear impact on the ranking of the climate models, which is highly dependent on the observational data set, the index and the metric used. The results highlight the need to be aware of the properties of observational data chosen in order to avoid overconfident and misleading conclusions with respect to climate model performance.

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