GCM Model Selection Procedure for Downscaling

2021 ◽  
Author(s):  
Carolyne Pickler ◽  
Thomas Mölg
Keyword(s):  
Surface Temperature ◽  
Regional Climate Model ◽  
East Africa ◽  
Spatial Resolution ◽  
General Circulation ◽  
Climate Model ◽  
Regional Climate ◽  
Selection Procedure ◽  
Reanalysis Data ◽  
Local Climate

<p>Downscaling has been widely used in studies of regional and/or local climate as it yields greater spatial resolution than general circulation models (GCM) can provide.  It can approached in two distinct ways: 1) Statistical and 2) Dynamical.  Statistical downscaling utilizes mathematical relationships between large-scale and regional/local climate to transform GCM or reanalysis data to a higher spatial resolution.  Dynamical downscaling comprises forcing the lateral boundaries of a regional climate model with reanalysis or GCM data.  However, there is no set technique to select said GCM(s).</p><p>A comprehensive yet easily applicable selection procedure was created to address this.  Using reanalysis data and/or observational data, the space-time climatic anomalies and the mean state of the climate are evaluated for the region of interest.  East Africa was utilized as a case study and GISS-E2-H r6i1p3 was found to perform the strongest.  This procedure cannot, however, tell whether the models can reproduce the key processes of the region.  To examine this, the ability of the models to simulate the Indian Ocean Dipole were evaluated.  It was found that higher ranked models were better able to capture it than lower ranked ones.  Furthermore, to ensure that a higher ranked model yielded a better downscaling simulation, three 10-year regional climate model simulations over East Africa were undertaken, where they were respectively forced by the highest ranked GCM (GISS-E2-H r6i1p3), the lowest ranked GCM (IPSL-CM5A-LR r4i1p1) and the MERRA-2 reanalysis product.  The simulated surface temperature and precipitation for Equatorial East Africa were compared with a gridded observational dataset (CRU TS 4.04).  Results showed that the higher ranked GCM produced a better downscaled simulation than the lower ranked one, a result that was more evident for surface temperature than precipitation.</p>

scholarly journals Evidence and analysis of 2012 Greenland records from spaceborne observations, a regional climate model and reanalysis data

2013 ◽  
Vol 7 (2) ◽  
pp. 615-630 ◽  
Author(s):  
M. Tedesco ◽  
X. Fettweis ◽  
T. Mote ◽  
J. Wahr ◽  
P. Alexander ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Regional Climate Model ◽  
Mass Balance ◽  
Climate Model ◽  
Regional Climate ◽  
Ice Sheet ◽  
Reanalysis Data ◽  
Surface Mass Balance ◽  
Surface Mass ◽  
The Mean

Abstract. A combined analysis of remote sensing observations, regional climate model (RCM) outputs and reanalysis data over the Greenland ice sheet provides evidence that multiple records were set during summer 2012. Melt extent was the largest in the satellite era (extending up to ∼97% of the ice sheet) and melting lasted up to ∼2 months longer than the 1979–2011 mean. Model results indicate that near surface temperature was ∼3 standard deviations (σ) above the 1958–2011 mean, while surface mass balance (SMB) was ∼3σ below the mean and runoff was 3.9σ above the mean over the same period. Albedo, exposure of bare ice and surface mass balance also set new records, as did the total mass balance with summer and annual mass changes of, respectively, −627 Gt and −574 Gt, 2σ below the 2003–2012 mean. We identify persistent anticyclonic conditions over Greenland associated with anomalies in the North Atlantic Oscillation (NAO), changes in surface conditions (e.g., albedo, surface temperature) and preconditioning of surface properties from recent extreme melting as major driving mechanisms for the 2012 records. Less positive if not increasingly negative SMB will likely occur should these characteristics persist.

scholarly journals Avaliação do modelo regional climático para a previsão de temperatura no centro de lançamento de Alcântara

2021 ◽  
Vol 3 (5) ◽  
pp. 2908-2921
Author(s):  
Alexandre Santos De Souza ◽  
Cleber Souza Correa ◽  
Inácio Malmonge Martin
Keyword(s):  
Surface Temperature ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Operations Planning ◽  
Weather Forecasts ◽  
Average Maximum ◽  
Medium Range ◽  
Maximum Temperatures

Este estudo avaliou a previsão intrasazonal da temperatura à superfície (2 m de altura) na região do Centro de Lançamento de Alcântara (CLA) utilizando O Regional Climate Model RegCM4.7 em comparação aos dados de reanálises obtidos do modelo de reanalises globalERA5 do European Centre for Medium-Range Weather Forecasts (ECMWF) para os meses de abril de 2019 (estação chuvosa) e outubro de 2019 (estação seca). Foram realizadas 4 (quatro) membros de simulações de temperatura utilizando o RegCM4.7 em horários sinóticos, os quais foram comparadas com os dados observacionais e de reanálises do ERA5. Os resultados indicaram uma boa previsibilidade para a temperatura média nos dois períodos, diferenças inferiores a 1 °C, com um grau de diferença para abril e praticamente coincidindo em outubro. Para as temperaturas máximas médias o RegCM4.7 superestimou em 2 °C para abril e 4 °C para outubro. Para as temperaturas médias mínimas subestimou em 2 °C tanto para abril como para outubro. Essas avaliações indicaram um bom desempenho geral para a previsão de temperaturas médias, contudo, sabendo-se da tendência de superestimar temperaturas máximas médias e subestimar temperaturas mínimas médias, ainda assim, com as devidas correções, poderá ser utilizado com eficácia para a previsão intrasazonal de temperatura à superfície em apoio ao planejamento de operações de lançamento no CLA.   This study evaluated the intraseasonal surface temperature (2 m height) forecast in the Alcântara Launch Center (ALC) region using the Regional Climate Model RegCM4.7 against reanalysis data obtained from the European Center for Medium-Range Weather global Forecasts (ECMWF) reanalysis model ERA5 for the months of April 2019 (rainy season) and October 2019 (dry season). Four members of temperature simulations were performed using RegCM4.7 at synoptic times, which were compared with observational and reanalysis data from ERA5. The results indicated a good predictability for the average temperature in the two periods, differences below 1 °C, with one degree of difference for April and practically coinciding in October. For average maximum temperatures RegCM4.7 overestimated by 2 °C for April and 4 °C for October. For average minimum temperatures, it was underestimated by 2 °C for both April and October. These evaluations indicated a good overall performance for predicting average temperatures, however, knowing the tendency to overestimate average maximum temperatures and underestimate average minimum temperatures, even so, with the appropriate corrections, it can be used effectively for forecasting intraseasonal surface temperature analysis in support of ALC launch operations planning.  

scholarly journals Summer drought in Switzerland 1981‒2020: Events, trends and drivers

2021 ◽  
Author(s):  
Simon C. Scherrer ◽  
Christoph Spirig ◽  
Martin Hirschi ◽  
Felix Maurer ◽  
Sven Kotlarski
Keyword(s):  
Regional Climate Model ◽  
Soil Water ◽  
Climate Model ◽  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Summer Drought ◽  
Temperature And Precipitation ◽  
Temperature Scaling

<p>The Alpine region has recently experienced several dry summers with negative impacts on the economy, society and ecology. Here, soil water, evapotranspiration and meteorological data from several observational and model-based data sources is used to assess events, trends and drivers of summer drought in Switzerland in the period 1981‒2020. 2003 and 2018 are identified as the driest summers followed by somewhat weaker drought conditions in 2020, 2015 and 2011. We find clear evidence for an increasing summer drying in Switzerland. The observed climatic water balance (-39.2 mm/decade) and 0-1 m soil water from reanalysis (ERA5-Land: -4.7 mm/decade; ERA5: -7.2 mm/decade) show a clear tendency towards summer drying with decreasing trends in most months. Increasing evapotranspiration (potential evapotranspiration: +21.0 mm/decade; ERA5-Land actual evapotranspiration: +15.1 mm/decade) is identified as important driver which scales excellently (+4 to +7%/K) with the observed strong warming of about 2°C. An insignificant decrease in precipitation further enhanced the tendency towards drier conditions. Most simulations of the EURO-CORDEX regional climate model ensemble underestimate the changes in summer drying. They underestimate both, the observed recent summer warming and the small decrease in precipitation. The changes in temperature and precipitation are negatively correlated, i.e. simulations with stronger warming tend to show (weak) decreases in precipitation. However, most simulations and the reanalysis overestimate the correlation between temperature and precipitation and the precipitation-temperature scaling on the interannual time scale. Our results emphasize that the analysis of the regional summer drought evolution and its drivers remains challenging especially with regional climate model data but considerable uncertainties also exist in reanalysis data sets.</p>

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

Atmosphere ◽  
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.

A Regional Climate Model Laboratory for Understanding Coastal Climate

2021 ◽  
Author(s):  
Travis O'Brien ◽  
Thomas Burkle ◽  
Michael Krauter ◽  
Thomas Trapp
Keyword(s):  
Steady State ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Cloud Fraction ◽  
Theoretical Physics ◽  
Coastal Regions ◽  
Local Climate ◽  
Coastal Climate ◽  
Mean Climate

<p>Midlatitude western coastal regions are recognized as being important for the global energy cycle, marine and terrestrial biodiversity, and regional economies.  These coastal regions exhibit a rich range of weather and climate phenomena, including persistent stratocumulus clouds, sea-breeze circulations, coastally-trapped Kelvin waves, and wind-driven upwelling. During the summer season, when impacts from transient synoptic systems are relatively reduced, the local climate is governed by a complex set of interactions among the atmosphere, land, and ocean.  This complexity has so far inhibited basic understanding of the drivers of western coastal climate, climate variability, and climate change.</p><p>As a way of simplifying the system, we have developed a hierarchical regional climate model experimental framework focused on the western United States. We modify the International Centre for Theoretical Physics RegCM4 to use steady-state initial, lateral, and top-of-model boundary conditions: average July insolation (no diurnal cycle) and average meteorological state (winds, temperature, humidity, surface pressure).  This July <em>Base State</em> simulation rapidly reaches a steady state solution that closely resembles the observed mean climate and the mean climate achieved using RegCM4 in a standard reanalysis-driven configuration.  It is particularly notable that the near-coastal stratocumulus field is spatially similar to the satellite-observed stratocumulus field during arbitrary July days: including gaps in stratocumulus coverage downwind of capes. We run similar <em>Base State</em> simulations for the other calendar months and find that these simulations mimic the annual cycle.  This suggests that the summer coastal stratocumulus field results from the steady-state response of the marine boundary layer to summertime climatological forcing; if true for the real world, this would imply that stratocumulus cloud fraction, within a given month, is temporally modulated by deviations from the summer base state (e.g., transient synoptic disturbances that interrupt the cloud field).  We describe modifications to this simplified experimental framework aimed at understanding the factors that govern stratocumulus cloud fraction and its variability.</p>

Impact of different and time-varying land cover data sets in a regional climate model on regional and local climate over Europe

2020 ◽  
Author(s):  
Mingyue Zhang ◽  
Jürgen Helmert ◽  
Merja Tölle
Keyword(s):  
Land Cover ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Data Sets ◽  
Time Varying ◽  
Data Set ◽  
Local Climate ◽  
Land Cover Data ◽  
Simulation Results

<p>According to IPCC, Land use and Land Cover (LC) changes have a key role to adapt and mitigate future climate change aiming to stabilize temperature rise up to 2°C. Land surface change at regional scale is associated to global climate change, such as global warming. It influences the earth’s water and energy cycles via influences on the heat, moisture and momentum transfer, and on the chemical composition of the atmosphere. These effects show variations due to different LC types, and due to their spatial and temporal resolutions.  Thus, we incorporate a new time-varying land cover data set based on ESACCI into the regional climate model COSMO-CLM(v5.0). Further, the impact on the regional and local climate is compared to the standard operational LC data of GLC2000 and GlobCover 2009. Convection-permitting simulations with the three land cover data sets are performed at 0.0275° horizontal resolution over Europe for the time period from 1992 to 2015.</p><p>Overall, the simulation results show comparable agreement to observations. However, the simulation results based on GLC2000 and GlobCover 2009 (with 23 LC types) LC data sets show a fluctuation of 0.5K in temperature and 5% of precipitation. Even though the LC is classified into the same types, the difference in LC distribution and fraction leads to variations in climate simulation results. Using all of the 37 LC types of the ESACCI-LC data set show noticeable differences in distribution of temperature and precipitation compared to the simulations with GLC2000 and GlobCover 2009. Especially in forest areas, slight differences of the plant cover type (e.g. Evergreen or Deciduous) could result in up to 10% differences (increase or decrease) in temperature and precipitation over the simulation domain. Our results demonstrate how LC changes as well as different land cover type effect regional climate. There is need for proper and time-varying land cover data sets for regional climate model studies. The approach of including ESACCI-LC data set into regional climate model simulations also improved the external data generation system.</p><p>We anticipate this research to be a starting point for involving time-varying LC data sets into regional climate models. Furthermore, it will give us a possibility to quantify the effect of time-varying LC data on regional climate accurately.</p><p><strong>Acknowledgement</strong>:</p><p>1: Computational resources were made available by the German Climate Computing Center (DKRZ) through support from the Federal Ministry of Education and Research in Germany (BMBF). We acknowledge the funding of the German Research Foundation (DFG) through grant NR. 401857120.</p><p>2: Appreciation for the support of Jürg Luterbacher and Eva Nowatzki.</p><p> </p>

scholarly journals Antarctic 20th Century Accumulation Changes Based on Regional Climate Model Simulations

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Klaus Dethloff ◽  
Ksenia Glushak ◽  
Annette Rinke ◽  
Dörthe Handorf
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
East Antarctica ◽  
Transient Pressure ◽  
Near Surface ◽  
Surface Mass ◽  
The Antarctic

The regional climate model HIRHAM has been applied to Antarctica driven at the lateral and lower boundaries by European Reanalysis data ERA-40 for the period 1958–1998. Simulations over 4 decades, carried out with a horizontal resolution of 50 km, deliver a realistic simulation of the Antarctic atmospheric circulation, synoptic-scale pressure systems, and the spatial distribution of precipitation minus sublimation (P-E) structures. The simulated P-E pattern is in qualitative agreement with glaciological estimates. The estimated (P-E) trends demonstrate surfacemass accumulation increase at the West Antarctic coasts and reductions in parts of East Antarctica. The influence of the Antarctic Oscillation (AAO) on the near-surface climate and the surface mass accumulation over Antarctica have been investigated on the basis of ERA-40 data and HIRHAM simulations. It is shown that the regional accumulation changes are largely driven by changes in the transient activity around the Antarctic coasts due to the varying AAO phases. During positive AAO, more transient pressure systems travelling towards the continent, and Western Antarctica and parts of South-Eastern Antarctica gain more precipitation and mass. Over central Antarctica the prevailing anticyclone causes a strengthening of polar desertification connected with a reduced surface mass balance in the northern part of East Antarctica.

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