the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Design, evaluation and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
Abstract. NARCliM2.0 comprises two Weather Research and Forecasting (WRF) regional climate models (RCMs) downscaling five CMIP6 global climate models contributing to the Coordinated Regional Downscaling Experiment over Australasia at 20 km resolution, and south-east Australia at 4 km convection-permitting resolution. We first describe NARCliM2.0’s design, including selecting two, definitive RCMs via testing seventy-eight RCMs using different parameterisations for planetary boundary layer, microphysics, cumulus, radiation, and land surface model (LSM). We then assess NARCliM2.0's skill in simulating the historical climate versus CMIP3-forced NARCliM1.0 and CMIP5-forced NARCliM1.5 RCMs and compare differences in future climate projections. RCMs using the new Noah-MP LSM in WRF with default settings confer substantial improvements in simulating temperature variables versus RCMs using Noah-Unified. Noah-MP confers smaller improvements in simulating precipitation, except for large improvements over Australia’s southeast coast. Activating Noah-MP’s dynamic vegetation cover and/or runoff options primarily improve simulation of minimum temperature. NARCliM2.0 confers large reductions in maximum temperature bias versus NARCliM1.0 and 1.5 (1.x), with small absolute biases of ~0.5 K over many regions versus over ~2 K for NARCliM1.x. NARCliM2.0 reduces wet biases versus NARCliM1.x by as much as 50 %, but retains dry biases over Australia’s north. NARCliM2.0 is biased warmer for minimum temperature versus NARCliM1.5 which is partly inherited from stronger warm biases in CMIP6 versus CMIP5 GCMs. Under shared socioeconomic pathway (SSP)3-7.0, NARCliM2.0 projects ~3 K warming by 2060–79 over inland regions versus ~2.5 K over coastal regions. NARCliM2.0-SSP3-7.0 projects dry futures over most of Australia, except for wet futures over Australia’s north and parts of western Australia which are largest in summer. NARCliM2.0-SSP1-2.6 projects dry changes over Australia with only few exceptions. NARCliM2.0 is a valuable resource for assessing climate change impacts on societies and natural systems and informing resilience planning by reducing model biases versus earlier NARCliM generations and providing more up-to-date future climate projections utilising CMIP6.
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CC1: 'Comment on gmd-2024-87', Jatin Kala, 15 May 2024
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Due to an oversight at submission stage, we did not include a link to a frozen version of the source code for WRF used in this project, as well as the configuration files for the simulations. This has now been rectified and all this information is now available on zenodo at: https://doi.org/10.5281/zenodo.11184830
We apologize for this oversight
Citation: https://doi.org/10.5194/gmd-2024-87-CC1 -
RC1: 'Comment on gmd-2024-87', Anonymous Referee #1, 23 Jun 2024
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Comments on the manuscript entitled “Design, evaluation and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble” by Virgilio et al. submitted to GMD
General comments:
The authors have compared the experimental designs and results across three generations of NARCliM RCMs. The latest iteration, NARCliM 2.0, features enhanced spatial resolution and utilizes CMIP6 experiment outputs as large-scale forcing data, representing advancements over earlier phases. The ensemble simulations of NARCliM 2.0 were conducted after a rigorous evaluation and selection process involving CMIP6 models and various physics configurations of the WRF model. This approach has the potential to provide more robust projections of regional climate over Australia. The ensemble simulations, incorporating diverse GCM-RCM combinations, make significant contributions to CORDEX. Therefore, I recommend acceptance pending minor revisions, including clarifications, correction, and reorganization in certain sections. Specific comments are outlined below:
Specific comments:
L108: Please replace "NARCliM2.0" with "NARCliM 2.0 (NARCliM 1.5)".
Section 3.2.1: It is unclear which variables were evaluated to assess CMIP6 GCM performance. Note that precipitation, daily maximum and minimum surface air temperatures do not serve as boundary conditions for driving the RCM. It would be preferable to evaluate U, V, T, Q, Z, SST, PSL for dynamical downscaling purposes. This issue should be properly addressed or discussed.
Table 2: Please clarify how many GCM-RCM runs were conducted for CORDEX-CMIP6 NARCliM 2.0. Specify the combinations used. Were all five GCMs downscaled by seven RCMs each? Presenting this information in a table format would aid readers in quickly accessing these details.
L423-424: The authors employed a cold restart for the SSP experiments. Did the authors examined the duration required for deep soil spin-up? Why not use soil moisture from a historical RCM run in 2014 or ERA5 reanalysis as initial conditions for the SSP experiments?
Section 4 Evaluation methods: these evaluation methods were already used in previous sections. It would improve clarity to present this section earlier in the manuscript.
L453-456: RMSE and PSS are typically used to assess model performance in simulating individual variables. However, it remains unclear how overall RCM performance in simulating multiple variables is determined. Did the authors normalized the biases/RMSEs when sum them together? Otherwise, the biases/RMSEs are in different order of magnitude. The authors may consider employing the Model Climate Performance Index (Gleckler et al., 2008) or multivariable integrated skill score (Zhang et al., 2021) for a comprehensive assessment in terms of the model performance in simulating multiple variables.
L699: Please replace "CMPI6" with "CMIP6".
L707-712: Could you explain why projected changes in TAS exhibit distinct spatial patterns between NARCliM 2.0 and NARCliM 1.5/1.0?
Fig.15: The quality of this figure appears low. Why do the stippling areas form very regular circles in the many subpanels, e.g., b, c, e, n, p, t, u, v? Consider presenting these figures as supplementary material and summarizing the statistics using a Taylor diagram.
L804-816: These discussions are somewhat tangential to the study's main focus and could be shortened or omitted. Instead, further investigate/discuss the differences in projected changes in the surface air temperature and precipitation among the three generations of NARCliM. For example, explore why widespread wet biases observed in NARCliM 1.x are substantially reduced in NARCliM 2. Are these biases attributable to GCMs, RCMs, or both?
References
Gleckler et al. 2008: Performance metrics for climate models, J. Geophys. Res., 113, D06104, https://doi.org/10.1029/2007JD008972
Xu Z. & Han Y., 2019: Comments on ‘DISO: A rethink of Taylor diagram’. International Journal of Climatology.40, 2506-2510
Zhang et al. 2021: An improved multivariable integrated evaluation method and tool (MVIETool) v1.0 for multimodel intercomparison, Geosci. Model Dev. https://doi.org/10.5194/gmd-14-3079-2021
Citation: https://doi.org/10.5194/gmd-2024-87-RC1
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