A Look at the Present- Day Arc2c Atmosphere in CCSM4

Transcription

1 A Look at the Present- Day Arc2c Atmosphere in CCSM4 Gijs de Boer 1,2,3, Bill Chapman 4, Jennifer Kay 5, Brian Medeiros 5, Ma8hew Shupe 1,2, Steve Vavrus 6 and John Walsh 7 (1) (2) (3) (4) (5) (6) (7) (Photo: PNNL)

2 Objec2ve: To analyze and document the ability of CCSM4 to simulate the present- day ( ) ArcDc Atmosphere Photo: Erlend Hermansen

3 Objec2ve: To analyze and document the ability of CCSM4 to simulate the present- day ( ) ArcDc Atmosphere - Six historical CMIP5 CCSM4 simuladons (5 ensemble members + MOAR) - MOAR ( Mother of All Runs ) provides high frequency output - Historical Runs are run from 185- (at least) 25, and are forced by observed changes in atmospheric composidon, solar forcing, surface emissions and land use changes

4 Objec2ve: To analyze and document the ability of CCSM4 to simulate the present- day ( ) ArcDc Atmosphere - Six historical CMIP5 CCSM4 simuladons (5 ensemble members + MOAR) - MOAR ( Mother of All Runs ) provides high frequency output - Historical Runs are run from 185- (at least) 25, and are forced by observed changes in atmospheric composidon, solar forcing, surface emissions and land use changes - Key properdes evaluated: - Tsfc - Boundary Layer Stability - SLP - Cloud Cover - The Atmospheric Energy Budget - PrecipitaDon/EvaporaDon - Document the ability of CCSM4 to correctly simulate variability within the system for some of these variables

5 Objec2ve: To analyze and document the ability of CCSM4 to simulate the present- day ( ) ArcDc Atmosphere - Six historical CMIP5 CCSM4 simuladons (5 ensemble members + MOAR) - MOAR ( Mother of All Runs ) provides high frequency output - Historical Runs are run from 185- (at least) 25, and are forced by observed changes in atmospheric composidon, solar forcing, surface emissions and land use changes - Key properdes evaluated: - Tsfc - Boundary Layer Stability - SLP - Cloud Cover - The Atmospheric Energy Budget - PrecipitaDon/EvaporaDon - Document the ability of CCSM4 to correctly simulate variability within the system for some of these variables

6 Journal of Climate Special Issue on CCSM4 (36 proposed ar2cles): h8p://

7 Area of Study: Map courtesy of M. Jakobsson (modified) (de Boer et al., 211)

8 Surface Air Temperature: RMSE (K) Bias ( ) J F M A M J J A S O N D Month

9 Surface Air Temperature: RMSE (K) Bias ( ) J F M A M J J A S O N D Month (Chapman and Walsh, 27)

10 Surface Air Temperature: $*& -! **! 3/. ##3- %2! + ")!3 3rd Santa Fe Conference on Global and Regional Climate Change +

11 Surface Air Temperature Variability:

12 Lower Tropospheric Stability: 2 16 DJF Ocean MAM % of total cases JJA SON DJF T (K) Land MAM % of total cases JJA SON T (K) CCSM4 CCSM3 CMIP3 ERA-Int ERA-4

13 % of total cases % of total cases Lower Tropospheric Stability: DJF JJA DJF JJA Ocean MAM SON T (K) Land MAM SON T (K) CCSM4 CCSM3 CMIP3 ERA-Int ERA-4 Relative Frequency 6-hourly Inv. Strength (K/km) CCSM4 ERA Jan Feb Mar Apr 85% 86% 85% 68% 69% 65% May Jun Jul Aug 58% 42% 55% 15% 11% 23% Sep Oct Nov Dec 55% 71% 8% 15% 31% 58% % 46% 48% 15% 83% 65% Inversion Strength (K/km)

14 Sea Level Pressure compared to CCSM3: RMSE (mb) Bias ( ) J F M A M J J A S O N D Month

15 Sea Level Pressure compared to CCSM3: RMSE (mb) (Chapman and Walsh, 27) Bias ( ) J F M A M J J A S O N D Month

16 Sea Level Pressure: $*& -!- **! 3/. ##3- %2! MB ")!3 MB 3rd Santa Fe Conference on Global and Regional Climate Change

17 Sea Level Pressure Impact on Sea Ice: Polar Pathfinder CCSM4 Δ Concentra2on (Jahn et al., 211)

18 Clouds: - ObservaDonal datasets peak during different Dmes. - CCSM4 tends to underesdmate cloud occurrence during most of the year (except summer months) - Low clouds are pardcularly underesdmated during all but summer months - CloudSAT/CALIPSO dataset courtesy of Jennifer Kay - SATEST courtesy of Steve Vavrus (includes essmates from ISCCP, TOVS Path- B, HIRS, MODIS, PATMOS, Wang and Key, and CERES) - GRDEST courtesy of Steve Vavrus (includes COADS, Huschke, Hahn et al. and Makshtas et al.) - SHEBA/Bar/Eur courtesy of Ma8hew Shupe

19 Clouds: 3 Barrow All- sky cloud liquid/ice water paths compared to surface observadon stadons Water Path (gm -2 ) Eureka SHEBA - CCSM4 liquid water path is too high for all locadons, though seasonal cycle is captured - CCSM4 ice water path is generally too low (except for Eureka winter) - IWP seasonal cycle does not appear to be captured in the simuladons Despite a lack of clouds (CF), liquid clouds that are present are found to be too thick, pardcularly during summer Liquid CCSM4 Month Ice OBS

20 Energy Budget: (Nakamura and Oort, 1988) -JRA/NRA and CERES from Porter et al. (21) -NRA ( ) and ERA-4 ( ) from Serreze et al. (27)

21 Height (km) 1 Cloud Phase: Liquid Ice CCSM4s1 Observations CCSM4s1-Obs Std(2yr_s1) Barrow Mixed Total % % Temperature dependent scheme. - StraSform condensate forms if RH grid >RH (RH =8% over land and 9% over ocean) - Any formed condensate is parssoned by temperature following: Std(ensemble) Fb Ap Jn Au Oc De Fb Ap Jn Au Oc De Fb Ap Jn Au Oc De Fb Ap Jn Au Oc De Month % 1 5 where: = - 4C = - 1C

22 Height (km) 1 Cloud Phase: Liquid Ice CCSM4s1 Observations CCSM4s1-Obs Std(2yr_s1) Barrow Mixed Total % % Temperature dependent scheme. - StraSform condensate forms if RH grid >RH (RH =8% over land and 9% over ocean) - Any formed condensate is parssoned by temperature following: f i = T T max T min T max Std(ensemble) Fb Ap Jn Au Oc De Fb Ap Jn Au Oc De Fb Ap Jn Au Oc De Fb Ap Jn Au Oc De Month % 1 5 where: T min T max = - 4C = - 1C (Zhang et al., 23)

23 Summary: - T sfc is well represented spadally and biases are generally small (<2K). - SLP fields do not match the reanalyses as closely, impacdng sea ice and ocean fields - The simulated lower troposphere is significantly more stable than its representadon in reanalyses - Cloud occurrence is generally undersimulated, compared to surface and satellite- based observadons - Clouds do demonstrate a seasonal cycle of sorts - Cloud liquid water paths are generally overesdmated compared to ground- based esdmates, while ice water paths are underesdmated - Cloud phase is found to vary from that observed from ground- based sensors. - Combined, the cloud biases impact the summerdme atmospheric energy budget

24 References: Boé, J., A. Hall and X. Qu (29): Current GCMs UnrealisSc NegaSve Feedback in the ArcSc, J. Clim., 22, Chapman, W.L. and J.E Walsh (27): SimulaSons of ArcSc Temperature and Pressure by Global Coupled Models, J. Clim., 2, de Boer, G., W. Chapman, J. Kay, B. Medeiros, M.D. Shupe, S. Vavrus and J.E. Walsh (211): A CharacterizaSon of the Present- Day ArcSc Atmosphere in CCSM4, J. Clim., accepted. Jahn, A., K. Sterling, M.M. Holland, J.E. Kay, J. Maslanik, C. Bitz, D. Bailey, E. Hunke, B. Lipscomb, D. Pollak and U. Bha8 (211): Late 2th Century SimulaSon of ArcSc Sea- Ice and Ocean ProperSes in the CCSM4, J. Clim., accepted, doi: /JCLI- D Liu, J., Z. Zhang, Y. Hu, L. Chen, Y. Dai and X. Ren (28): Assessment of Surface Air Temperature over the ArcSc Ocean in Reanalysis and IPCC AR4 Model SimulaSons with IABP/POLES ObservaSons, J. Geophys. Res., 113, D115. Nakamura, N. and A.H. Oort (1988): Atmospheric Heat Budgets of the Polar Regions, J. Geophys. Res., 93, Porter, D.F., J.J. Cassano, M.C. Serreze and D.N. Kindig (21): New EsSmates of the Large- Scale ArcSc Atmospheric Energy Budget, J. Geophys. Res., 115, D818. Serreze, M.C., A.P. Barre8, A.G. Slater, M. Steele, J. Zhang and K.E. Trenberth (27): The Large- Scale Energy Budget of the ArcSc, J. Geophys. Res., 112, D , A.P. Barre8 and F. Lo (25): Northern High- LaStude PrecipitaSon as Depicted by Atmospheric Reanalyses and Satellite Retrievals, Mon. Wea. Rev., 133, Shupe, M.D. and J.M. Intrieri (24): Cloud RadiaSve Forcing of the ArcSc Surface: The Influence of Cloud ProperSes, Surface Albedo, and Solar Zenith Angle, J. Clim., 17, Walsh, J.E., W.L. Chapman and D.H. PorSs (28): ArcSc Cloud FracSon and RadiaSve Fluxes in Atmospheric Reanalyses, J. Clim., 22, Wilson, A.B., D.H. Bromwich and K.M. Hines (211): EvaluaSon of Polar WRF Forecasts on the ArcSc System Reanalysis Domain. Part I: Surface and Upper Air Analysis, J. Geophys. Res., submi\ed. Zhang, M., W. Lin, C.S. Bretherton, J.J. Hack and P.J. Rasch (23): A modified formulason of fracsonal strasform condensason rate in the NCAR community atmospheric model CAM2, J. Geophys. Res., 18, 435.