A Concept to Assess the Performance. with a Climate Model

Transcription

1 A Concept to Assess the Performance of a Permafrost Model run fully Coupled with a Climate Model Debasish PaiMazumder & Nicole Mölders Acknowledgements My committee members US Bhatt, G Kramm and JE Walsh for academic and research guidance, encouragement, helpful comments and discussions The International Arctic Research Center, UAF for funding, under the auspices of the NSF cooperative p agreement g OPP and ARC ARSC and NCAR for computational support

2 Motivation The better understanding of feedbacks between permafrost, weather, climate, and other potential impacts requires adequate soil-temperature simulations in NWPMs, CTMs, GCMs, and ESMs Choice of initial conditions, discretization, grid resolution, numerical scheme, parameterizations, model assumptions, and/or empirical parameters may cause an incorrect simulation of atmospheric forcing The non-uniform distribution and/or coarseness of observational networks may introduce biases in regional averages

3 Research hypothesis The performance of a permafrost model fully coupled with a climate model depends partly on the permafrost model itself, the accuracy of the forcing data and design of observational network Questions to be addressed How well does a fully coupled climate model simulate soil- temperature? What are the reasons for the discrepancies between simulated and observed soil-temperature? How much of the discrepancies between simulated and observation-based gridded soil-temperature climatologies can be explained by the observational network density and/or design?

4 Description of Community Climate System Model version 3.0 atm lnd cpl ice ocn Resolution: T42 (2.8 o X2.8 o ) PaiMazumder D, Miller J, Li Z, Walsh JE, Etringer A, McCreight J, Zhang T, Mölders N (2008) Evaluation of Community Climate System Model soil temperatures using observations from Russia. Theor Appl Climatol 94:

5 Methods Quantification of model errors by calculating the BIAS, Root Mean Square Error (RMSE), & Standard Deviation Error (SDE) Comparison of CCSM3-simulated & observed soil temperature at 0.2, 0.4, 0.8, 1.6 and 3.2 m depths Evaluation of simulated near-surface air temperature, cloud fraction, precipitation and snow depth with respect to ERA40 reanalysis, ISCCP, GPCC and NSIDC snow depth data Sensitivity studies on role of vegetation & soil types Using WRF simulations to create the reference dataset to assess the impact of network design on regional averages

6 CCSM3 overestimates winter and underestimates summer soil temperatures ure (K) temperatu Soil t Month Simulated soil temperature Observed soil temperature Modified after PaiMazumder et al. 2008

7 CCSM3 captures the annual average soil temperatures reasonably well ature (K) oil tempera mulated so Sim Observed soil temperature (K) Modified after PaiMazumder et al. 2008

8 Questions to be addressed How well does a fully coupled climate model simulate soil-temperature? What are the reasons for the discrepancies between simulated and observed soil-temperature? How much of the discrepancies between simulated and observation-based gridded soil-temperature climatologies l i can be explained by the observational network density and/or design?

9 Model surface air temperature biases minimally impact soil temperature Near-surface air temperatures are slightly overestimated in winter, but underestimated for other seasons Second climatology Third climatology CCSM air temperature ER40 air temperature CCSM3 captures near-surface temperature reasonably well Modified after PaiMazumder et al. 2008

10 Cloud fraction and precipitation: major contributors to underestimation of soil temperature in summer Cloud fraction Cloud fraction is underestimated in summer Precipitation CCSM3 underestimates precipitation in summer Simulated Observed RMSEs higher in summer than in winter Modified after PaiMazumder et al. 2008

11 Overestimated snow depth leads to overestimation of soil temperature in winter Simulated snow depth Observed snow depth Modified after PaiMazumder et al. 2008

12 RMSEs increase concomitantly from November to April (meter) RMSEs higher in western & southwestern part of Russia (between 50 o N and 70 o N, 35 o E to 90 o E) in winter Modified after PaiMazumder et al. 2008

13 A 3% vegetation change or 10% soil-type change marginally affect soil temperature No significance difference between original simulation & modified simulation with 3% vegetation change Original simulation Modified simulation No obvious overall advantage for one or the other choice of soil parameters Modified after PaiMazumder et al. 2008

14 Questions to be addressed How well does a fully coupled climate model simulate soil-temperature? What are the reasons for the discrepancies between simulated and observed soil-temperature? How much of the discrepancies between simulated and observation-based gridded soil-temperature climatologies l i can be explained by the observational network density and/or design?

15 WRF simulations are used to create a reference dataset The WRF model domain encompasses Siberia by grid-points WRF grid-increment is 50km with 31 vertical layers from the surface to 50hPa and six layers inside the soil Simulations have been performed for July and December 2005, 2006 and 2007 PaiMazumder D, Mölders N (2009) Theoretical assessment of uncertainty in regional averages due to network density and design. J Appl Meteor Climatol (in press)

16 Reference: Regional average for areas of 2.8 o x2.8 o determined from the WRF output 10,500 grid points of WRF over Siberia Topography This reference dataset consists of 637 regional averages for 2.8 o x2.8 o areas

17 Artificial networks: 10 sets of four networks with 500, 400, 200, and 100 arbitrarily taken WRF grid-cells 500-sites-network t 400-sites-network t 200-sites-network 100-sites-network

18 Real network: Regional averages for an area o x2.8 o determined for the 411 sites Here WRF-simulated data are used NO observational data is used Zhang et al. 2001

19 Real network misrepresents the landscape Reference 500-sites-network 400-sites-network 200-sites-network 100-sites-network Real network Mixed forest Water bodies The real network overestimates the fraction of mixed forest by 15% and underestimates the water-bodies by 10%

20 Real network has difficulties to reproduce the reference regional average of soil temperature K K Reference 400 sites Real network Modified after PaiMazumder and Mölders et al. 2009

21 In winter, CCSM-bias can be partially explained by uncertainties due to network density CCSM3 soil temperature bias is higher in winter than summer m) Depth ( Months Out of 6K CCSM3 bias, 2K may result from uncertainty due network design Soil te emperature e (K) at 0.2m 7 Errors in CCSM-simulation Bias,Dec Bias, Jul RMSE, Dec RMSE, Jul Error due to incorrect simulation of forcings Errors due to network design PaiMazumder D, Mölders N (2008) Sources of discrepancy between CCSM simulated and gridded observation-based soiltemperature over Siberia: The influence of site density and distribution. 9 th International Conference on Permafrost (NICOP):

22 Summary CCSM3 captures the phase and the annual average of soiltemperatures well but fails to capture the amplitude Inaccurate simulation of cloud fraction, precipitation and snow depth are major contributors to discrepancies in simulated soil temperature Soil characteristics contribute notably or even significantly to the errors in the simulated soil-temperature climatologies Networks with 200 or more randomly distributed sites reliably reproduce reference regional averages while real network has difficulties in capturing the reference regional averages

23 Application Climate: air temperature, precipitation, cloudiness REF ALB TEM Total aboveground vegetation ti biomass Albedo anomaly CCSM3 REF CCSM3 ALB CCSM3 ALBCO2 B2 scenario ALBCO 2 Near surface temperature ALB-REF(JJA)

24 Conclusion CCSM3 simulates warm soil processes of the active layer better than frozen soil processes Convective clouds and precipitation parameterization shortcomings may be the main reason for the underestimation of summer soil-temperatures Some discrepancies between CCSM3-simulated and gridded soil-temperatures are due to differences between assumed and actual soil and vegetation characteristics Non-random network design introduces substantial uncertainty in gridded data

25 Conclusion (Cont.) Less number of randomly placed sites provide better results than a large number of ill placed sites, intelligent network design may save costs and increases the knowledge Future networks should be designed in a more spatially random method Evaluation studies using long-term data taken for other purposes require to develop intelligent strategies to guarantee meaningful conclusions on model performance and for model improvement Performance of a soil/permafrost model fully coupled with a climate model depends partly on the soil/permafrost model itself, the accuracy of the forcing data provided by the climate model and the design of the observational network

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