Evaluation of Agriculture-Related Climate Indices in Hindcast COSMO-CLM Simulations over Central Europe

High horizontal resolution regional climate model simulations serve as forcing data for crop and dynamic vegetation models, for generating possible scenarios of the future effects of climate change on crop yields and pollinators. Here, we performed convection-permitting hindcast simulations with the regional climate model COSMO5.0-CLM15 (CCLM) from 1979 to 2015, and the first year was considered as a spin-up period. The model was driven with hourly ERA5 data, which were the latest climate reanalysis product by ECMWF, and directly downscaled to a 3 km horizontal resolution over Central Europe. The land-use classes were described by ECOCLIMAP, and the soil type and depth were described by HWSD. The evaluation was carried out in terms of temperature, precipitation, and climate indices, comparing CCLM output with the gridded observational dataset HYRAS from the German Weather Service. While CCLM inherits a warm and dry summer bias found in its parent model, it reproduces the main features of the recent past climate of Central Europe, including the seasonal mean climate patterns and probability density distributions. Furthermore, the model reproduced climate indices for temperature like growing season length, growing season start date, number of summer days. The results highlighted the possibility of directly downscaling ERA5 data with regional climate models, avoiding the multiple nesting approach and high computational costs. This study adds confidence to convection-permitting climate projections of future changes in agricultural climate indices.

Download Full-text

Evaluation of agricultural-related extreme events in hindcast COSMO-CLM simulations over Central Europe

10.5194/egusphere-egu21-9507 ◽

2021 ◽

Author(s):

Huan Zhang ◽

Merja Tölle

Keyword(s):

Regional Climate Model ◽

Central Europe ◽

Extreme Events ◽

Climate Models ◽

Climate Model ◽

Crop Yields ◽

Regional Climate ◽

Extreme Weather ◽

Horizontal Resolution ◽

Extreme Weather Events

Convection-permitting regional climate model simulations may serve as driving data for crop and dynamic vegetation models. It is thus possible to generate physically consistent scenarios for the future-concerning effects of climate change on crop yields and pollinators. Here, we performed convection-permitting hindcast simulations with the regional climate model COSMO5.0-CLM16 (CCLM) from 1980 to 2015 with a spin-up starting at 1979. The model was driven with hourly ERA5 data, which is the latest climate reanalysis product by ECMWF and directly downscaled to 3 km horizontal resolution over central Europe. The land-use classes are described by ECOCLIMAP, and the soil type and depth by HWSD. The evaluation is carried out in terms of temperature, precipitation, and extreme weather indices, comparing CCLM output with the gridded observational dataset HYRAS from the German Weather Service. While CCLM inherits a warm/cold and dry/wet summer/winter bias found in its parent model, it reproduces the main features of the present climate of the study domain, including the distribution, the seasonal mean climate patterns, and probability density distributions. The bias for precipitation ranges between &#177;20 % and the bias for temperature between &#177;1 &#176;C compared to the observations over most of the regions. This is in the range of the bias between observational data. Furthermore, the model catches extreme weather events related to droughts, floods, heat/cold waves, and agriculture-specific events. The results highlight the possibility to directly downscale ERA5 data with regional climate models avoiding the multiple nesting approach and high computational costs. This study adds confidence to convection-permitting climate simulations of future changes in agricultural extreme events.

Download Full-text

Long-term variance of heavy precipitation across central Europe using a large ensemble of regional climate model simulations

Earth System Dynamics ◽

10.5194/esd-11-469-2020 ◽

2020 ◽

Vol 11 (2) ◽

pp. 469-490

Author(s):

Florian Ehmele ◽

Lisa-Ann Kautz ◽

Hendrik Feldmann ◽

Joaquim G. Pinto

Keyword(s):

Regional Climate Model ◽

Central Europe ◽

Climate Model ◽

Extreme Values ◽

Regional Climate ◽

Horizontal Resolution ◽

Small Scale ◽

Data Sets ◽

Large Ensemble

Abstract. Widespread flooding events are among the major natural hazards in central Europe. Such events are usually related to intensive, long-lasting precipitation over larger areas. Despite some prominent floods during the last three decades (e.g., 1997, 1999, 2002, and 2013), extreme floods are rare and associated with estimated long return periods of more than 100 years. To assess the associated risks of such extreme events, reliable statistics of precipitation and discharge are required. Comprehensive observations, however, are mainly available for the last 50–60 years or less. This shortcoming can be reduced using stochastic data sets. One possibility towards this aim is to consider climate model data or extended reanalyses. This study presents and discusses a validation of different century-long data sets, decadal hindcasts, and also predictions for the upcoming decade combined to a new large ensemble. Global reanalyses for the 20th century with a horizontal resolution of more than 100 km have been dynamically downscaled with a regional climate model (Consortium for Small-scale Modeling – CLimate Mode; COSMO-CLM) towards a higher resolution of 25 km. The new data sets are first filtered using a dry-day adjustment. Evaluation focuses on intensive widespread precipitation events and related temporal variabilities and trends. The presented ensemble data are within the range of observations for both statistical distributions and time series. The temporal evolution during the past 60 years is captured. The results reveal some long-term variability with phases of increased and decreased precipitation rates. The overall trend varies between the investigation areas but is mostly significant. The predictions for the upcoming decade show ongoing tendencies with increased areal precipitation. The presented regional climate model (RCM) ensemble not only allows for more robust statistics in general, it is also suitable for a better estimation of extreme values.

Download Full-text

Panama’s Current Climate Replicability in a Non-Hydrostatic Regional Climate Model Nested in an Atmospheric General Circulation Model

Atmosphere ◽

10.3390/atmos12121543 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1543

Author(s):

Reinhardt Pinzón ◽

Noriko N. Ishizaki ◽

Hidetaka Sasaki ◽

Tosiyuki Nakaegawa

Keyword(s):

Regional Climate Model ◽

Air Temperature ◽

General Circulation ◽

Climate Model ◽

Regional Climate ◽

Surface Air Temperature ◽

Circulation Model ◽

Horizontal Resolution ◽

Temperature And Precipitation ◽

Current Climate

To simulate the current climate, a 20-year integration of a non-hydrostatic regional climate model (NHRCM) with grid spacing of 5 and 2 km (NHRCM05 and NHRCM02, respectively) was nested within the AGCM. The three models did a similarly good job of simulating surface air temperature, and the spatial horizontal resolution did not affect these statistics. NHRCM02 did a good job of reproducing seasonal variations in surface air temperature. NHRCM05 overestimated annual mean precipitation in the western part of Panama and eastern part of the Pacific Ocean. NHRCM05 is responsible for this overestimation because it is not seen in MRI-AGCM. NHRCM02 simulated annual mean precipitation better than NHRCM05, probably due to a convection-permitting model without a convection scheme, such as the Kain and Fritsch scheme. Therefore, the finer horizontal resolution of NHRCM02 did a better job of replicating the current climatological mean geographical distributions and seasonal changes of surface air temperature and precipitation.

Download Full-text

Assessing the Effects of Spatial Resolution on Regional Climate Model Simulated Summer Temperature and Precipitation in China: A Case Study

Advances in Meteorology ◽

10.1155/2016/7639567 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 4

Author(s):

Xin-Min Zeng ◽

Ming Wang ◽

Yujian Zhang ◽

Yang Wang ◽

Yiqun Zheng

Keyword(s):

Regional Climate Model ◽

South China ◽

Climate Model ◽

Regional Climate ◽

Surface Air Temperature ◽

Northern China ◽

Horizontal Resolution ◽

Vertical Resolution ◽

Humid Climate ◽

Entire Area

The regional climate model, RegCM3, is used to simulate the 2004 summer surface air temperature (SAT) and precipitation at different horizontal (i.e., 30, 60, and 90 km) and vertical resolutions (i.e., 14, 18, and 23 layers). Results showed that increasing resolution evidently changes simulated SATs with regional characteristics. For example, simulated SATs are apparently better produced when horizontal resolution increases from 60 to 30 km under the 23 layers. Meanwhile, the SATs over the entire area are more sensitive to vertical resolution than horizontal resolution. The subareas present higher sensitivities than the total area, with larger horizontal resolution effects than those of vertical resolution. For precipitation, increasing resolution shows higher impact compared to SAT, with higher sensitivity induced by vertical resolution than by horizontal resolution, especially in rainy South China. The best SAT/precipitation can be produced only when the horizontal and vertical resolutions are reasonably configured. This indicates that different resolutions lead to different atmospheric thermodynamic states. Because of the dry climate and low soil heat capacity in Northern China, resolution changes easily modify surface energy fluxes, hence the SAT; due to the rainy and humid climate in South China, resolution changes likely strongly influence grid-scale structure of clouds and therefore precipitation.

Download Full-text

Future snowfall in western and central Europe projected with a high-resolution regional climate model ensemble

Geophysical Research Letters ◽

10.1002/2014gl059724 ◽

2014 ◽

Vol 41 (12) ◽

pp. 4294-4299 ◽

Cited By ~ 17

Author(s):

Hylke de Vries ◽

Geert Lenderink ◽

Erik van Meijgaard

Keyword(s):

High Resolution ◽

Regional Climate Model ◽

Central Europe ◽

Climate Model ◽

Regional Climate ◽

Model Ensemble

Download Full-text

Adaptation and Application of the large LAERTES-EU RCM Ensemble for Hydrological Modeling

10.5194/egusphere-egu21-9598 ◽

2021 ◽

Author(s):

Florian Ehmele ◽

Lisa-Ann Kautz ◽

Hendrik Feldmann ◽

Yi He ◽

Martin Kadlec ◽

...

Keyword(s):

Regional Climate Model ◽

Central Europe ◽

Hydrological Modeling ◽

Climate Model ◽

Regional Climate ◽

Added Value ◽

Data Set ◽

Model Simulations ◽

Climate Model Simulations ◽

Rhine Catchment

Enduring and extensive heavy precipitation associated with widespread river floods are among the main natural hazards affecting Central Europe. Since such events are characterized by long return periods, it is difficult to adequately quantify their frequency and intensity solely based on the available observations of precipitation. Furthermore, long-term observations are rare, not homogeneous in space and time, and thus not suitable to run hydrological models (HMs). To overcome this issue, we make use of the recently introduced LAERTES-EU (LArge Ensemble of Regional climaTe modEl Simulations for EUrope) data set, which is an ensemble of regional climate model simulations providing 12.000 simulated years. LAERTES-EU is adapted and applied for the use in an HM to calculate discharges for large river catchments in Central Europe, where the Rhine catchment serves as the pilot area for calibration and validation. Quantile mapping with a fixed density function is used to correct the bias in model precipitation. The results show clear improvements in the representation of both precipitation (e.g., annual cycle and intensity distributions) and simulated discharges by the HM after the bias correction. Furthermore, the large size of LAERTES-EU improves the statistical representativeness also for high return values of precipitation and discharges. While for the Rhine catchment a clear added value is identified, the results are more mixed for other catchments (e.g., the Upper Danube).

Download Full-text

Comparison of different infiltration schemes in the regional climate model REMO

10.5194/egusphere-egu21-9874 ◽

2021 ◽

Author(s):

Daniel Abel ◽

Katrin Ziegler ◽

Felix Pollinger ◽

Heiko Paeth

Keyword(s):

Regional Climate Model ◽

Climate Models ◽

Climate Model ◽

Root Zone ◽

Regional Climate ◽

Climate Projections ◽

Soil Hydrology ◽

Spatiotemporal Resolution ◽

Development Fund ◽

Near Surface

The European Regional Development Fund-Project BigData@Geo aims to create highly resolved climate projections for the model region of Lower Franconia in Bavaria, Germany. These projections are analyzed and made available to local stakeholders of agriculture, forestry, and viniculture as well as general public. Since regional climate models&#8217; spatiotemporal resolution often is too coarse to deal with such local issues, the regional climate model REMO is improved within the frame of the project in cooperation with the Climate Service Center Germany (GERICS).Accurate and highly resolved climate projections require realistic modeling of soil hydrology. Thus, REMO&#8217;s original bucket scheme is replaced by a 5-layer soil scheme. It allows for the representation of water below the root zone. Evaporation is possible solely from the top layer instead of the entire bucket and water can flow vertically between the layers. Consequently, the properties and processes change significantly compared to the bucket scheme. Both, the bucket and the 5-layer scheme, use the improved Arno scheme to separate throughfall into infiltration and surface runoff.In this study, we examine if this scheme is suitable for use with the improved soil hydrology or if other schemes lead to better results. For this, we (1) modify the improved Arno scheme and further introduce the infiltration equations of (2) Philip as well as (3) Green and Ampt. First results of the comparison of these four different schemes and their influence on soil moisture and near-surface atmospheric variables are presented.

Download Full-text