%0 Journal Article %1 rai2022performance %A Rai, P. %A Ziegler, K. %A Abel, D. %A Pollinger, F. %A Paeth, H. %D 2022 %J Theoretical and Applied Climatology %K article myown %N 3 %P 1385--1405 %R 10.1007/s00704-022-04233-y %T Performance of a regional climate model with interactive vegetation (REMO-iMOVE) over Central Asia %U https://doi.org/10.1007/s00704-022-04233-y %V 150 %X The current study evaluates the regional climate model REMO (v2015) and its new version REMO-iMOVE, including interactive vegetation and plant functional types (PFTs), over two Central Asian domains for the period of 2000--2015 at two different horizontal resolutions (0.44° and 0.11°). Various statistical metrices along with mean bias patterns for precipitation, temperature, and leaf area index have been used for the model evaluation. A better representation of the spatial pattern of precipitation is found at 0.11° resolution over most of Central Asia. Regarding the mean temperature, both model versions show a high level of agreement with the validation data, especially at the higher resolution. This also reduces the biases in maximum and minimum temperature. Generally, REMO-iMOVE shows an improvement regarding the temperature bias but produces a larger precipitation bias compared to the REMO conventional version with interannually static vegetation. Since the coupled version is capable to simulate the mean climate of Central Asia like its parent version, both can be used for impact studies and future projections. However, regarding the new vegetation scheme and its spatiotemporal representation exemplified by the leaf area index, REMO-iMOVE shows a clear advantage over REMO. This better simulation is caused by the implementation of more realistic and interactive vegetation and related atmospheric processes which consequently add value to the regional climate model.

@article{rai2022performance, abstract = {The current study evaluates the regional climate model REMO (v2015) and its new version REMO-iMOVE, including interactive vegetation and plant functional types (PFTs), over two Central Asian domains for the period of 2000--2015 at two different horizontal resolutions (0.44{\textdegree} and 0.11{\textdegree}). Various statistical metrices along with mean bias patterns for precipitation, temperature, and leaf area index have been used for the model evaluation. A better representation of the spatial pattern of precipitation is found at 0.11{\textdegree} resolution over most of Central Asia. Regarding the mean temperature, both model versions show a high level of agreement with the validation data, especially at the higher resolution. This also reduces the biases in maximum and minimum temperature. Generally, REMO-iMOVE shows an improvement regarding the temperature bias but produces a larger precipitation bias compared to the REMO conventional version with interannually static vegetation. Since the coupled version is capable to simulate the mean climate of Central Asia like its parent version, both can be used for impact studies and future projections. However, regarding the new vegetation scheme and its spatiotemporal representation exemplified by the leaf area index, REMO-iMOVE shows a clear advantage over REMO. This better simulation is caused by the implementation of more realistic and interactive vegetation and related atmospheric processes which consequently add value to the regional climate model.}, added-at = {2023-12-18T18:34:20.000+0100}, author = {Rai, P. and Ziegler, K. and Abel, D. and Pollinger, F. and Paeth, H.}, biburl = {https://www.bibsonomy.org/bibtex/2d5d6ccff3afa67a4b5b0963073383aba/fepo}, day = 01, doi = {10.1007/s00704-022-04233-y}, interhash = {57b387382fdc585217b8372c5ab66964}, intrahash = {d5d6ccff3afa67a4b5b0963073383aba}, issn = {1434-4483}, journal = {Theoretical and Applied Climatology}, keywords = {article myown}, month = nov, number = 3, pages = {1385--1405}, timestamp = {2023-12-22T15:09:21.000+0100}, title = {Performance of a regional climate model with interactive vegetation (REMO-iMOVE) over Central Asia}, url = {https://doi.org/10.1007/s00704-022-04233-y}, volume = 150, year = 2022 }