CCSM3 simulations

• Format: Dataset
• Language: unknown
• Published: Arctic Data Center
• Subjects: CCSM3 simulations; World; permafrost
• Online Access: https://search.dataone.org/view/878dec57-39a1-43f5-a394-2ca4e312af86

Soil-temperatures simulated by the fully coupled Community Climate System Model version 3.0 (CCSM3) are evaluated using three gridded Russian soil-temperature climatologies (1951-1980, 1961-1990, and 1971-2000) to assess the performance of permafrost and/or soil simulations. CCSM3 captures the annual phase of the soiltemperature cycle well, but not the amplitude. It provides slightly too high (low) soiltemperatures in winter (summer) with a better performance in summer than winter. In winter, soil-temperature biases reach up to 6 K. Simulated near-surface air temperatures agree well with the near-surface air temperatures from reanalysis data. Discrepancies in CCSM3-simulated near-surface air temperatures significantly correlate with discrepancies in CCSM3-simulated soiltemperatures, i.e. contribute to discrepancy in soil-temperature simulation. Evaluation of cloud-fraction by means of the International Satellite Cloud Climatology project data reveals that errors in simulated cloud fraction explain some of the soil-temperature discrepancies in summer. Evaluation by means of the Global Precipitation Climatology Centre data identifies inaccurately-simulated precipitation as a contributor to underestimating summer soil-temperatures. Comparison to snow-depth observations shows that overestimating snow-depth leads to winter soil-temperature overestimation. Sensitivity studies reveal that uncertainty in mineral-soil composition notably contributes to discrepancies between CCSM3-simulated and observed soil-temperature climatology while differences between the assumed vegetation in CCSM3 and the actual vegetation in nature marginally contribute to the discrepancies in soil-temperature. Out of the 6 K bias in CCSM3 soil-temperature simulation, about 2.5 K of the bias may result from the incorrect simulation of the observed forcing and about 2 K of the bias may be explained by uncertainties due network density in winter. This means that about 1.5 K winter-bias may result from measurement errors and/or model deficiencies. Overall, the performance of a permafrost/soil model fully coupled with a climate model depends partly on the permafrost/soil model itself, the accuracy of the forcing data and design of observational network.