Rongqian Yang, Ken Mitchell, Jesse Meng Impact of Different Land Models & Different Initial Land States on CFS Summer and Winter Reforecasts Acknowledgment.

Slides:



Advertisements
Similar presentations
American Monsoons-ENSO teleconnection Vasu Misra, Dept. of Earth, Ocean and Atmospheric Science, Florida State University 1.
Advertisements

Seasonal Climate Predictability over NAME Region Jae-Kyung E. Schemm CPC/NCEP/NWS/NOAA NAME Science Working Group Meeting 5 Puerto Vallarta, Mexico Nov.
Assessment of CFSv2 hindcast (seasonal mean) CPC/NCEP/NOAA Jan 2011.
Validation of the NCEP CFS forecasts Suranjana Saha Environmental Modeling Center NCEP/NWS/NOAA/DOC.
INTRODUCTION Although the forecast skill of the tropical Pacific SST is moderate due to the largest interannual signal associated with ENSO, the forecast.
California and Nevada Drought is extreme to exceptional.
Temperature (ºC) wind field at 200hPa Performance of the HadRM3P model for downscaling of present climate in South American Lincoln Muniz Alves*, José.
The NCEP operational Climate Forecast System : configuration, products, and plan for the future Hua-Lu Pan Environmental Modeling Center NCEP.
Hydrologic Predictability and Water Year 2009 Predictions in the Columbia River Basin Andy Wood Matt Wiley Bart Nijssen Climate and Water Resource Forecasts.
Seasonal outlooks for hydrology and water resources in the Pacific Northwest Andy Wood Alan Hamlet Dennis P. Lettenmaier Department of Civil and Environmental.
Transitioning unique NASA data and research technologies to the NWS 1 Evaluation of WRF Using High-Resolution Soil Initial Conditions from the NASA Land.
Alan F. Hamlet Andy Wood Seethu Babu Marketa McGuire Dennis P. Lettenmaier JISAO Climate Impacts Group and the Department of Civil Engineering University.
Seasonal outlooks for hydrology and water resources: streamflow, reservoir, and hydropower forecasts for the Pacific Northwest Andy Wood and Alan Hamlet.
Rongqian Yang Ken Mitchell, Jesse Meng, Helin Wei, George Gayno Acknowledgments to Suru Saha, Wanqiu Wang, Cathy Thiaw Environmental Modeling Center (EMC)
CPC’s U.S. Seasonal Drought Outlook & Future Plans April 20, 2010 Brad Pugh, CPC.
Land Surface Models & Surface Water Hydrology Cédric DAVID.
Warm Season Precipitation Predictions over North America with the Eta Regional Climate Model Model Sensitivity to Initial Land States and Choice of Domain.
Improved Land Modeling for Drought Monitoring and Seasonal Hydrological Prediction Including Groundwater Mickael Ek, Rongqian Yang, Youlong Xia, Jesse.
Potential Predictability of Drought and Pluvial Conditions over the Central United States on Interannual to Decadal Time Scales Siegfried Schubert, Max.
The Eta Regional Climate Model: Model Development and Its Sensitivity in NAMAP Experiments to Gulf of California Sea Surface Temperature Treatment Rongqian.
NCA-LDAS Meeting, Sept 23, 2014 NCA-LDAS: An Integrated Terrestrial Water Analysis System for the National Climate Assessment “Water Indicators” Hiroko.
UMAC data callpage 1 of 11NLDAS EMC Operational Models North American Land Data Assimilation System (NLDAS) Michael Ek Land-Hydrology Team Leader Environmental.
INDIA and INDO-CHINA India and Indo-China are other areas where the theoretical predictability using the interactive soil moisture is superior to the fixed.
The La Niña Influence on Central Alabama Rainfall Patterns.
EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Beyond seasonal forecasting F. J. Doblas-Reyes,
Enhancing the Value of GRACE for Hydrology
NW NCNE SCSESW Rootzone: TOTAL PERCENTILEANOMALY Noah VEGETATION TYPE 2-meter Column Soil Moisture GR2/OSU LIS/Noah 01 May Climatology.
1 NCEP Production Suite Review: “Land Surface Guidance Systems” EMC Land-Hydrology Team: Michael Ek, Jesse Meng, Rongqian Yang, Helin Wei, Youlong Xia,
NCEP Production Suite Review: Land-Hydrology at NCEP
Modification of GFS Land Surface Model Parameters to Mitigate the Near- Surface Cold and Wet Bias in the Midwest CONUS: Analysis of Parallel Test Results.
NCEP Production Suite Review: “Land Surface Guidance Systems” EMC Land-Hydrology Team: Michael Ek, Jesse Meng, Rongqian Yang, Helin Wei, Youlong Xia,
Rongqian Yang, Kenneth Mitchell, Jesse Meng NCEP Environmental Modeling Center (EMC) Summer and Winter Season Reforecast Experiments with the NCEP Coupled.
Progress of CTB Transition Project Team for Land Data Assimilation: Impact on CFS of: A) new land model (Noah LSM) B) new land initial conditions (from.
Ken Mitchell Jesse Meng, Rongqian Yang, Helin Wei, George Gayno NCEP Environmental Modeling Center (EMC) The Land Model and Land Assimilation of the CFS.
NOAA/Climate Prediction Center Outlooks for Spring-Summer, 2010 Ed O’Lenic Chief, Operations Branch NOAA-NWS-Climate Prediction Center Weatherbug Energy.
1 Climate Test Bed Seminar Series 24 June 2009 Bias Correction & Forecast Skill of NCEP GFS Ensemble Week 1 & Week 2 Precipitation & Soil Moisture Forecasts.
CPPA Past/Ongoing Activities - Ocean-Atmosphere Interactions - Address systematic ocean-atmosphere model biases - Eastern Pacific Investigation of Climate.
The climate and climate variability of the wind power resource in the Great Lakes region of the United States Sharon Zhong 1 *, Xiuping Li 1, Xindi Bian.
The European Heat Wave of 2003: A Modeling Study Using the NSIPP-1 AGCM. Global Modeling and Assimilation Office, NASA/GSFC Philip Pegion (1), Siegfried.
Retrospective Evaluation of the Performance of Experimental Long-Lead Columbia River Streamflow Forecasts Climate Forecast and Estimated Initial Soil Moisture.
Implementation and preliminary test of the unified Noah LSM in WRF F. Chen, M. Tewari, W. Wang, J. Dudhia, NCAR K. Mitchell, M. Ek, NCEP G. Gayno, J. Wegiel,
Current and Future Initialization of WRF Land States at NCEP Ken Mitchell NCEP/EMC WRF Land Working Group Workshop 18 June 2003.
NOAA’s Climate Prediction Center & *Environmental Modeling Center Camp Springs, MD Impact of High-Frequency Variability of Soil Moisture on Seasonal.
1. Introduction 2. The model and experimental design 3. Space-time structure of systematic error 4. Space-time structure of forecast error 5. Error growth.
The lower boundary condition of the atmosphere, such as SST, soil moisture and snow cover often have a longer memory than weather itself. Land surface.
NARCCAP WRF Simulations L. Ruby Leung Pacific Northwest National Laboratory NARCCAP Users Meeting February , 2008 Boulder, CO.
CTB computer resources / CFSRR project Hua-Lu Pan.
Recent and planed NCEP climate modeling activities Hua-Lu Pan EMC/NCEP.
Dynamic Hurricane Season Prediction Experiment with the NCEP CFS Jae-Kyung E. Schemm January 21, 2009 COLA CTB Seminar Acknowledgements: Lindsey Long Suru.
One-year re-forecast ensembles with CCSM3.0 using initial states for 1 January and 1 July in Model: CCSM3 is a coupled climate model with state-of-the-art.
Hydrologic Forecasting Alan F. Hamlet Dennis P. Lettenmaier JISAO/CSES Climate Impacts Group Dept. of Civil and Environmental Engineering University of.
Initialization of the Noah Land Surface Model and its Coupling to CFS Ken Mitchell, Rongqian Yang, Jesse Meng and EMC Land Team Environmental Modeling.
Report on CTB CFS Test and Evaluation Team Activities Team Leads: Jae-Kyung Schemm and Shrinivas Moorthi CPC and EMC, NCEP/NWS/NOAA 32nd Climate Diagnostics.
1 An Assessment of the CFS real-time forecasts for Wanqiu Wang, Mingyue Chen, and Arun Kumar CPC/NCEP/NOAA.
1 Xiaoyan Jiang, Guo-Yue Niu and Zong-Liang Yang The Jackson School of Geosciences The University of Texas at Austin 03/20/2007 Feedback between the atmosphere,
1 Yun Fan, Huug van den Dool, Dag Lohmann, Ken Mitchell CPC/EMC/NCEP/NWS/NOAA Kunming, May, 2004.
Land surface memory & hydrological cycle over the U.S. west coast states & monsoon region Yun Fan and Huug van del Dool CPC/NCEP/NOAA
1 A review of CFS forecast skill for Wanqiu Wang, Arun Kumar and Yan Xue CPC/NCEP/NOAA.
Rongqian Yang Ken Mitchell, Jesse Meng, Helin Wei, George Gayno NCEP Environmental Modeling Center Summer Season Predictions with the Next NCEP CFS Using.
Principal Investigator: Siegfried Schubert
Canadian Seasonal to Interannual Prediction System (CanSIPS)
Andrew Wood, Ali Akanda, Dennis Lettenmaier
Improving the Land Surface Component of the CFS Reanalysis
Improving the Land Surface Component of the CFS Reanalysis
Introduction to Land Information System (LIS)
Shuhua Li and Andrew W. Robertson
Hydrology and Water Management Applications of GCIP Research
Progress in Seasonal Forecasting at NCEP
University of Washington Center for Science in the Earth System
1 GFDL-NOAA, 2 Princeton University, 3 BSC, 4 Cerfacs, 5 UCAR
Presentation transcript:

Rongqian Yang, Ken Mitchell, Jesse Meng Impact of Different Land Models & Different Initial Land States on CFS Summer and Winter Reforecasts Acknowledgment to : S. Saha, S. Moorthi, W. Wang, C. Thiaw This development is sponsored by CPPA Program of the NOAA Climate Program Office 4 th Annual Climate Test Bed Science Advisory Board Meeting September 2008

Project Sponsorship This work is funded by the Climate Prediction Program for the Americas (CPPA) of the NOAA Climate Program Office. No funding was received from the Climate Test Bed (CTB) program for this work, but the experimental design and importance was evaluated positively by CTB technical advisory group. The CFS experiments were executed in the CTB partition of the NOAA Research Computer at NCEP.

Objective of this project: Upgrade the land physics and initial land states of the CFS and assess the impact on T126 CFS summer and winter season reforecasts. Motivation: While SST anomalies are believed to be the foremost source of seasonal predictability in coupled global models, land surface anomalies are generally believed to be the second most important source of seasonal predictability (e.g. anomalies of soil moisture, snowpack, vegetation cover)

Land Model Upgrade: In CFS experiments Noah LSM (new) versus OSU LSM (old): Noah LSM –4 soil layers (10, 30, 60, 100 cm) –Frozen soil physics included –Surface fluxes weighted by snow cover fraction –Improved seasonal cycle of vegetation cover –Spatially varying root depth –Runoff and infiltration account for sub-grid variability in precipitation & soil moisture –Improved soil & snow thermal conductivity –Higher canopy resistance –More OSU LSM –2 soil layers (10, 190 cm) –No frozen soil physics –Surface fluxes not weighted by snow fraction –Vegetation fraction never less than 50 percent –Spatially constant root depth –Runoff & infiltration do not account for subgrid variability of precipitation & soil moisture –Poor soil and snow thermal conductivity, especially for thin snowpack and moist soils Noah LSM replaced OSU LSM in operational NCEP medium-range Global Forecast System (GFS) in late May 2005

Initial Land States: Two Sources GLDAS/Noah & Global Reanalysis 2 (GR2/OSU): GLDAS: an uncoupled land assimilation system driven by observed precipitation analyses (CPC CMAP analyses) –Executed using same grid, land mask, terrain field and four-layer Noah LSM as in experimental CFS forecasts –Non-precipitation land forcing is from GR2 –Executed retrospectively from (after spin-up) GR2: a coupled atmosphere/land assimilation system wherein land component is driven by model predicted precipitation –applies the OSU LSM with two soil layers –nudges soil moisture based on differences between model and CPC CMAP precipitation

Observed 90-day Precipitation Anomaly (mm) valid 30 April 99 GLDAS/Noah (top ) versus GR2/OSU (bottom) 2-meter soil moisture (% volume) May 1 st Climatology 01 May 1999 Anomaly Left column: GLDAS/Noah soil moisture climo is generally higher then GR2/OSU Middle column: GLDAS/Noah soil moisture anomaly pattern agrees better than that of GR2/OSU with observed precipitation anomaly (right column: top) GLDAS/Noah GR2/OSU

Monthly Time Series ( ) of Area-mean Illinois 2-meter Soil Moisture [mm]: Observations (black), GLDAS/Noah (purple), GR2/OSU (green) Climatology The climatology of GLDAS/Noah soil moisture is higher and closer to the observed climatology than that of GR2/OSU.

Choice of Land Model Choice of Land Initial Conditions GR2/OSU (CONTROL)GLDAS/Noah GLDAS/Noah--CLIMO GR2/OSU CFS/NoahCFS/OSU Summer CFS Experiments: all 4 configurations above (A, B, C, D) 25-year ( ) summer reforecasts (10 member ensembles) from mid April and early May initial conditions Winter Land Related Experiments: top 2 configurations in table (A & C) 24-year ( ) winter reforecasts (10 member ensembles) from late Nov and Dec initial conditions Four configurations of T126 CFS: A) CFS/OSU/GR2: - OSU LSM, initial land states from GR2 (CONTROL)‏ B) CFS/Noah/GR2: - Noah LSM, initial land states from GR2 C) CFS/Noah/GLDAS: - Noah LSM, initial land states from T126 GLDAS/Noah D) CFS/Noah/GLDAS-Climo: - Noah LSM, initial land states from GLDAS/Noah climo CFS Experiment Design: four configurations

Summer Results 25-year ( ) summer reforecasts (10 member ensembles) from mid April and early May initial conditions

Partition 25 summers (80-04) into Neutral & Non-neutral samples using MJJ Nino3.4 SST anomaly 0.7C as a threshold 10 non-neutral summers: 82,83,87,88,91,92,93,97,99,02 (red: warm, blue: cold) 15 neutral summers: 80,81,84,85,86,89,90,94,95,96,98,00,01,03,04

10 non-neutral years: CONUS JJA precipitation AC score Worst Case

15 neutral years: CONUS JJA precipitation AC score Worst Case

Significance test (T-statistic) shows differences wrt third bar are not significant at 90% confidence. Significance test (T-statistic) shows differences wrt third bar are significant at 90% confidence. Non-Neutral Years Neutral Years CONUS-average JJA precipitation AC score

Winter Results Only two of four configurations were executed: -- OSU/GR2 (Control) -- Noah/GLDAS 24-year ( ) winter reforecasts (10 member ensembles) from late Nov and Dec initial conditions

Partition 24 winters ( ) into Neutral & Non-neutral samples using JFM Nino3.4 SST anomaly 0.5C as a threshold 14 non-neutral winters: 83, 85, 86, 87, 88, 89, 92, 95, 96, 98, 99, 00, 01, neutral winters: 81, 82, 84, 90, 91, 93, 94, 97, 02, 04

14 Non-neutral Years: CONUS JFM Precipitation AC Score: 83, 85, 86, 87, 88, 89, 92, 95, 96, 98, 99, 00, 01, Neutral Years: CONUS Domain JFM Precip AC Score: 81, 82, 84, 90, 91, 93, 94, 97, 02, 04

CONUS-average JFM precipitation AC score Non-neutral Years I Significance test (T-statistic) shows differences are not significant at 90% confidence Neutral Years Significance test (T-statistic) shows differences are not significant at 90% confidence

Conclusions When upgrading land surface model of coupled CFS, it is imperative to upgrade to the same land surface model in the supporting data assimilation system Positive impact of land surface upgrade on CFS seasonal forecast skill for precipitation is very modest –Significant only for summer season in neutral ENSO years (and then only very small positive impact) –Essentially neutral impact for winter season and non-neutral ENSO summers For a given land configuration, differences in CONUS precipitation skill between neutral and non-neutral years appears larger than differences between two different land configurations for given sample of years –Confirming that impact of SST anomaly is indeed substantially greater than impact of land surface configuration