An official website of the United States government
Evaluation of the University of Victoria Earth System Climate Model version 2.10 (UVic ESCM 2.10)
Abstract. The University of Victoria Earth System Climate Model (UVic ESCM) ofintermediate complexity has been a useful tool in recent assessments oflong-term climate changes, including both paleo-climate modelling anduncertainty assessments of future warming. Since the last official releaseof the UVic ESCM 2.9 and the two official updates during the last decade,considerable model development has taken place among multiple researchgroups. The new version 2.10 of the University of Victoria Earth SystemClimate Model presented here will be part of the sixth phaseof the Coupled Model Intercomparison Project (CMIP6). More precisely it willbe used in the intercomparison of Earth system models of intermediatecomplexity (EMIC), such as the C4MIP, the Carbon Dioxide Removal and ZeroEmissions Commitment model intercomparison projects (CDR-MIP and ZECMIP,respectively). It now brings together and combines multiple modeldevelopments and new components that have come about since the lastofficial release of the model. The main additions to the base model are(i) an improved biogeochemistry module for the ocean, (ii) a vertically resolvedsoil model including dynamic hydrology and soil carbon processes, and (iii) arepresentation of permafrost carbon. To set the foundation of its use, wehere describe the UVic ESCM 2.10 and evaluate results from transienthistorical simulations against observational data. We find that the UVicESCM 2.10 is capable of reproducing changes in historical temperature andcarbon fluxes well. The spatial distribution of many ocean tracers,including temperature, salinity, phosphate and nitrate, also agree well withobserved tracer profiles. The good performance in the ocean tracers isconnected to an improved representation of ocean physical properties. Forthe moment, the main biases that remain are a vegetation carbon density thatis too high in the tropics, a higher than observed change in the ocean heatcontent (OHC) and an oxygen utilization in the Southern Ocean that is too low.All of these biases will be addressed in the next updates to the model.
more »
« less
- Award ID(s):
- 2022461
- PAR ID:
- 10271589
- Date Published:
- Journal Name:
- Geoscientific Model Development
- Volume:
- 13
- Issue:
- 9
- ISSN:
- 1991-9603
- Page Range / eLocation ID:
- 4183 to 4204
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Abstract Global climate models (GCMs) and Earth system models (ESMs) exhibit biases, with resolutions too coarse to capture local variability for fine-scale, reliable drought and climate impact assessment. However, conventional bias correction approaches may cause implausible climate change signals due to unrealistic representations of spatial and intervariable dependences. While purely data-driven deep learning has achieved significant progress in improving climate and earth system simulations and predictions, they cannot reliably learn the circumstances (e.g., extremes) that are largely unseen in historical climate but likely becoming more frequent in the future climate (i.e., climate non-stationarity). This study shows an integrated trend-preserving deep learning approach that can address the spatial and intervariable dependences and climate non-stationarity issues for downscaling and bias correcting GCMs/ESMs. Here we combine the super-resolution deep residual network (SRDRN) with the trend-preserving quantile delta mapping (QDM) to downscale and bias correct six primary climate variables at once (including daily precipitation, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed) from five state-of-the-art GCMs/ESMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that the SRDRN-QDM approach greatly reduced GCMs/ESMs biases in spatial and intervariable dependences while significantly better-reducing biases in extremes compared to deep learning. The estimated drought based on the six bias-corrected and downscaled variables captured the observed drought intensity and frequency, which outperformed state-of-the-art multivariate bias correction approaches, demonstrating its capability for correcting GCMs/ESMs biases in spatial and multivariable dependences and extremes.more » « less
-
The dominant source of inter-model differences in comprehensive global climate models (GCMs) are cloud radiative effects on Earth's energy budget. Intermediate complexity models, while able to run more efficiently, often lack cloud feedbacks. Here, we describe and evaluate a method for applying GCM-derived shortwave and longwave cloud feedbacks from 4 × CO2 and Last Glacial Maximum experiments to the University of Victoria Earth System Climate Model. The method generally captures the spread in top-of-the-atmosphere radiative feedbacks between the original GCMs, which impacts the magnitude and spatial distribution of surface temperature changes and climate sensitivity. These results suggest that the method is suitable to incorporate multi-model cloud feedback uncertainties in ensemble simulations with a single intermediate complexity model.more » « less
-
Abstract A set of diagnostics based on simple, statistical relationships between precipitation and the thermodynamic environment in observations is implemented to assess phase 6 of the Coupled Model Intercomparison Project (CMIP6) model behavior with respect to precipitation. Observational data from the Atmospheric Radiation Measurement (ARM) permanent field observational sites are augmented with satellite observations of precipitation and temperature as an observational baseline. A robust relationship across observational datasets between column water vapor (CWV) and precipitation, in which conditionally averaged precipitation exhibits a sharp pickup at some critical CWV value, provides a useful convective onset diagnostic for climate model comparison. While a few models reproduce an appropriate precipitation pickup, most models begin their pickup at too low CWV and the increase in precipitation with increasing CWV is too weak. Convective transition statistics compiled in column relative humidity (CRH) partially compensate for model temperature biases—although imperfectly since the temperature dependence is more complex than that of column saturation. Significant errors remain in individual models and weak pickups are generally not improved. The conditional-average precipitation as a function of CRH can be decomposed into the product of the probability of raining and mean precipitation during raining times (conditional intensity). The pickup behavior is primarily dependent on the probability of raining near the transition and on the conditional intensity at higher CRH. Most models roughly capture the CRH dependence of these two factors. However, compensating biases often occur: model conditional intensity that is too low at a given CRH is compensated in part by excessive probability of precipitation.more » « less
-
Abstract The air–sea exchange of heat and carbon in the Southern Ocean (SO) plays an important role in mediating the climate state. The dominant role the SO plays in storing anthropogenic heat and carbon is a direct consequence of the unique and complex ocean circulation that exists there. Previous generations of climate models have struggled to accurately represent key SO properties and processes that influence the large-scale ocean circulation. This has resulted in low confidence ascribed to twenty-first-century projections of the state of the SO from previous generations of models. This analysis provides a detailed assessment of the ability of models contributed to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to represent important observationally based SO properties. Additionally, a comprehensive overview of CMIP6 performance relative to CMIP3 and CMIP5 is presented. CMIP6 models show improved performance in the surface wind stress forcing, simulating stronger and less equatorward-biased wind fields, translating into an improved representation of the Ekman upwelling over the Drake Passage latitudes. An increased number of models simulate an Antarctic Circumpolar Current (ACC) transport within observational uncertainty relative to previous generations; however, several models exhibit extremely weak transports. Generally, the upper SO remains biased warm and fresh relative to observations, and Antarctic sea ice extent remains poorly represented. While generational improvement is found in many metrics, persistent systematic biases are highlighted that should be a priority during model development. These biases need to be considered when interpreting projected trends or biogeochemical properties in this region.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/gmd-13-4183-2020
