Observing and modeling land-atmosphere interactions on...
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Observing and modeling land - atmosphere interactions on diurnal to seasonal timescales Craig R. Ferguson 1 ([email protected]) and Joshua K. Roundy 2 ([email protected]) 1 Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, USA 2 Department of Civil, Environmental, and Architectural Engineering, University of Kansas, Lawrence, KS, USA Overview : Several prior land surface initialization studies using the NASA-Unified Weather Research and Forecasting model (NU- WRF) (e.g., Case et al. 2008; Santanello et al. 2016) and idealized multi-global model ensemble experiments (e.g., Guo et al. 2006; Koster et al. 2010; Koster et al. 2004; Koster et al. 2006; Seneviratne et al. 2013; van den Hurk et al. 2012) have demonstrated the knock-on impact of realistic surface states on short-term to sub-seasonal surface air temperature and precipitation forecasts, respectively. The role of land-atmosphere (L-A) coupling is clearly isolated in these aforementioned modeled cases. However, an open question is: What is the realism of the modeled coupling? Due to observational constraints, models have been poorly, if ever evaluated for coupling realism (Roundy et al. 2014). What is further troubling is that even in so-called coupling ‘hot spots’, the inter-model spread is substantial (i.e., Koster et al. 2004; their Fig. 1 insets). As the climate modeling community moves from atmosphere-ocean global climate models (AOGCMs) to Earth System Models (ESMs) with dynamic vegetation and fully- coupled carbon, energy, and water cycles, realistic L-A coupling becomes of central importance. Objectives and Approaches : We are actively working with counterparts on the Global Land-Atmosphere System Study (GLASS) and Global Hydroclimatology (GHP) panels of the Global Energy and Water Exchanges Project (GEWEX) to meet the following (2) objectives: (1) Evaluate the realism of coupling in models and diagnose systematic biases. Approach : (a) Develop a multi-metric L-A coupling verification and diagnostic toolkit tailored for application with, for example, DOE Atmospheric Radiation Measurement Southern Great Plains (ARM- SGP), New York State Mesonet, or Coupled Model Intercomparison Project Phase 6 (CMIP-6) data. (b) Facilitate greater application (assimilation) of satellite remote sensing data (e.g., Soil Moisture Active Passive, Moderate Resolution Imaging Spectroradiometer) in model evaluation (simulations). (c) Perform process-oriented model sensitivity studies, especially in the context of circulation anomalies and land use/land cover change. (2) Improve our ability to measure L-A coupling across major climate zones at multiple scales. Approach : (a) Fill current L-A coupling related observing system gaps with ground station supplements (e.g., remote sensing atmospheric profilers at FLUXNET sites). (b) Conduct targeted seasonal (e.g., Ferguson et al. 2014) and multi-year field campaigns (e.g., GEWEX North American Regional Hydroclimate Project). (c) Secure enhanced spacebourne monitoring capabilities (e.g., Hyperspectral Environmental Sounder for: water vapor and temperature profiles in the lower troposphere, height of the PBL, and PBL entrainment, as a next Decadal Mission) In the figures that follow we present a few highlights from our most recent activities. Fig. 2(a) New York State Mesonet (http ://www.nysmesonet.org), to be completed by early 2017. Enhanced sites will have the full standard meteorological sensor suite, which includes Stevens HydraProbes at 5-, 25- and 50-cm depths, as well as: 4-component radiation, sonic anemometer, gas analyzer, ground heat flux, sun photometer (Q.Min; U-Albany), microwave radiometer (Radiometrics MP-3000A), and lidar (Leosphere Windcube 100s). (b) Skew-T diagram for Albany, NY, USA @ 00Z (1930 LT) 27 May 2016; deep PBL enabled by strong sensible heat flux and weak subsidence. Potential Temperature (K) Findell and Eltahir (2003) Frye and Mote (2010) Fig. 1 (a) DOE sponsored an Atmospheric Radiation Measurement (ARM) Enhanced Soundings for Local Coupling Studies Field Campaign (ESLCS; Ferguson et al. 2014), which took place at the ARM-SGP Central Facility from June 15 to August 31, 2015. During ESLCS, routine 4 times daily radiosonde measurements at the ARM-SGP CF were augmented on 12 days (June 18 and 29; July 11, 14, 19, and 26; August 15, 16, 21, 25, 26, and 27) with daytime 1-hourly radiosondes and 10-minute ‘trailer’ radiosondes every 3 hours. (b) 16 August 2015 PBL height estimates from a theta-v (potential temperature) gradient approach (black line) as well as other approaches included in the ARM PBLh Value-Added Product (Sivaraman et al. 2013). (c) 16 August 2015 potential temperature profiles for all ESLCS launches. (a) (b) (c) (a) (b) (a) (b) Fig. 3 (a) Frequency distribution of daily differences between NLDAS-2 afternoon (1400–1900 CST) and nonafternoon (0600–1300 CST Day 0 and from 2000 CST Day 0 to 0500 CST Day 1) P computed for the period of 1979–2014 (gray line; right y axis). (b) Diurnal cycle of NLDAS-2 P (mm/hr) over the SGP domain as a function of coupling event persistence and type (i.e., wet or dry coupling). The warm-season climatological (i.e., all days; sample size n=5424) diurnal P cycle is provided for reference (black line). Whiskers denote 95% confidence intervals. The underlying sample size (days) and the fractional composition of those days with AP precipitation is notated above. Taken from Song et al. (2016; their Figs. 4 and 5). Wet-coupling Dry-coupling Climatological frequency Fig. 4 (left) NARR-derived regime composites for the 24 h period preceding CTP-HI event classification for the SGP domain (0600 CST Day -1 to 0500 CST Day 0). Hashing represents difference from climatology significant at 95% CI. Taken from Song et al. (2016; their Fig. 2). Fig. 5 (right) Afternoon Peak (AP) precipitation probabilities across the distribution of identified explanatory variables. Taken from Song et al. (2016; their Fig. 7).
Transcript of Observing and modeling land-atmosphere interactions on...
Observing and modeling land-atmosphere interactions on diurnal to seasonal timescalesCraig R. Ferguson1 ([email protected]) and Joshua K. Roundy2 ([email protected]) 1Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, USA2Department of Civil, Environmental, and Architectural Engineering, University of Kansas, Lawrence, KS, USA
Overview: Several prior land surface initialization studies using
the NASA-Unified Weather Research and Forecasting model (NU-WRF) (e.g., Case et al. 2008; Santanello et al. 2016) and idealizedmulti-global model ensemble experiments (e.g., Guo et al. 2006;Koster et al. 2010; Koster et al. 2004; Koster et al. 2006;Seneviratne et al. 2013; van den Hurk et al. 2012) havedemonstrated the knock-on impact of realistic surface states onshort-term to sub-seasonal surface air temperature andprecipitation forecasts, respectively. The role of land-atmosphere(L-A) coupling is clearly isolated in these aforementioned modeledcases. However, an open question is: What is the realism of themodeled coupling? Due to observational constraints, models havebeen poorly, if ever evaluated for coupling realism (Roundy et al.2014). What is further troubling is that even in so-called coupling‘hot spots’, the inter-model spread is substantial (i.e., Koster et al.2004; their Fig. 1 insets). As the climate modeling communitymoves from atmosphere-ocean global climate models (AOGCMs)to Earth System Models (ESMs) with dynamic vegetation and fully-coupled carbon, energy, and water cycles, realistic L-A couplingbecomes of central importance.
Objectives and Approaches: We are actively working with
counterparts on the Global Land-Atmosphere System Study(GLASS) and Global Hydroclimatology (GHP) panels of the GlobalEnergy and Water Exchanges Project (GEWEX) to meet thefollowing (2) objectives:
(1) Evaluate the realism of coupling in models anddiagnose systematic biases.Approach: (a) Develop a multi-metric L-A coupling verification anddiagnostic toolkit tailored for application with, for example, DOEAtmospheric Radiation Measurement Southern Great Plains (ARM-SGP), New York State Mesonet, or Coupled Model IntercomparisonProject Phase 6 (CMIP-6) data. (b) Facilitate greater application(assimilation) of satellite remote sensing data (e.g., Soil MoistureActive Passive, Moderate Resolution Imaging Spectroradiometer) inmodel evaluation (simulations). (c) Perform process-orientedmodel sensitivity studies, especially in the context of circulationanomalies and land use/land cover change.
(2) Improve our ability to measure L-A coupling acrossmajor climate zones at multiple scales.Approach: (a) Fill current L-A coupling related observing systemgaps with ground station supplements (e.g., remote sensingatmospheric profilers at FLUXNET sites). (b) Conduct targetedseasonal (e.g., Ferguson et al. 2014) and multi-year field campaigns(e.g., GEWEX North American Regional Hydroclimate Project). (c)Secure enhanced spacebourne monitoring capabilities (e.g.,Hyperspectral Environmental Sounder for: water vapor andtemperature profiles in the lower troposphere, height of the PBL,and PBL entrainment, as a next Decadal Mission)
In the figures that follow we present a few highlights from ourmost recent activities.
Fig. 2(a) New York State Mesonet (http://www.nysmesonet.org), to be completed by early 2017. Enhanced sites will have the full standardmeteorological sensor suite, which includes Stevens HydraProbes at 5-, 25- and 50-cm depths, as well as: 4-component radiation, sonicanemometer, gas analyzer, ground heat flux, sun photometer (Q.Min; U-Albany), microwave radiometer (Radiometrics MP-3000A), and lidar(Leosphere Windcube 100s). (b) Skew-T diagram for Albany, NY, USA @ 00Z (1930 LT) 27 May 2016; deep PBL enabled by strong sensible heatflux and weak subsidence.
Potential Temperature (K)
Findell and Eltahir (2003) Frye and Mote (2010)
Fig. 1 (a) DOE sponsored an Atmospheric RadiationMeasurement (ARM) Enhanced Soundings for LocalCoupling Studies Field Campaign (ESLCS; Ferguson et al.2014), which took place at the ARM-SGP Central Facilityfrom June 15 to August 31, 2015. During ESLCS, routine 4times daily radiosonde measurements at the ARM-SGPCF were augmented on 12 days (June 18 and 29; July 11,14, 19, and 26; August 15, 16, 21, 25, 26, and 27) withdaytime 1-hourly radiosondes and 10-minute ‘trailer’radiosondes every 3 hours. (b) 16 August 2015 PBLheight estimates from a theta-v (potential temperature)gradient approach (black line) as well as otherapproaches included in the ARM PBLh Value-AddedProduct (Sivaraman et al. 2013). (c) 16 August 2015potential temperature profiles for all ESLCS launches.
(a)
(b) (c)
(a) (b)
(a) (b)
Fig. 3 (a) Frequency distribution of daily differences between NLDAS-2 afternoon (1400–1900 CST) and nonafternoon (0600–1300 CST Day 0and from 2000 CST Day 0 to 0500 CST Day 1) P computed for the period of 1979–2014 (gray line; right y axis). (b) Diurnal cycle of NLDAS-2 P(mm/hr) over the SGP domain as a function of coupling event persistence and type (i.e., wet or dry coupling). The warm-season climatological(i.e., all days; sample size n=5424) diurnal P cycle is provided for reference (black line). Whiskers denote 95% confidence intervals. Theunderlying sample size (days) and the fractional composition of those days with AP precipitation is notated above. Taken from Song et al.(2016; their Figs. 4 and 5).
Wet-coupling
Dry-coupling
Climatological frequency
Fig. 4 (left) NARR-derived regime composites for the 24 h period preceding CTP-HI event classification for the SGP domain (0600 CST Day -1 to0500 CST Day 0). Hashing represents difference from climatology significant at 95% CI. Taken from Song et al. (2016; their Fig. 2). Fig. 5 (right)Afternoon Peak (AP) precipitation probabilities across the distribution of identified explanatory variables. Taken from Song et al. (2016; theirFig. 7).
Ferguson, C.R. and J.K. Roundy (2016), Observing and modeling land-atmosphere interactions on diurnal to seasonal timescales, 6-2, Observations and Modeling Across Scales: Symposium in Honor of Eric Wood, Princeton, NJ, USA, June 2-3 2016.
REFERENCES Case, J. L., W. L. Crosson, S. V. Kumar, W. M. Lapenta, and C. D. Peters-Lidard, 2008: Impacts of
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Ferguson, C. R., J. Santanello, and P. Gentine, 2014: Enhanced Soundings for Local Coupling Studies: 2015 ARM Climate Research Facility Field Campaign. DOE/SC-ARM-15-035 , 8pgs, (Available: https://www.arm.gov/publications/programdocs/doe-sc-arm-15-035.pdf).
Guo, Z. C., and Coauthors, 2006: GLACE: The Global Land-Atmosphere Coupling Experiment. Part II: Analysis. J Hydrometeorol, 7, 611-625.
Koster, R. D., and Coauthors, 2010: Contribution of land surface initialization to subseasonal forecast skill: First results from a multi-model experiment. Geophys Res Lett, 37.
Koster, R. D., and Coauthors, 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 1138-1140.
Koster, R. D., and Coauthors, 2006: GLACE: The Global Land-Atmosphere Coupling Experiment. Part I: Overview. J Hydrometeorol, 7, 590-610.
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Seneviratne, S. I., and Coauthors, 2013: Impact of soil moisture-climate feedbacks on CMIP5 projections: First results from the GLACE-CMIP5 experiment. Geophys Res Lett, 40, 5212-5217.
Sivaraman, C., S. A. McFarlane, E. Chapman, M. Jensen, T. Toto, S. Liu, and M. Fischer, 2013: DOE/SC-ARM/TR-132: Planetary Boundary Layer (PBL) Height Value Added Product (VAP): Radiosonde Retrievals, https://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-132.pdf?id=899. 36.
Song, H.-J., C. R. Ferguson, and J. K. Roundy, 2016: Land–Atmosphere Coupling at the Southern Great Plains Atmospheric Radiation Measurement (ARM) Field Site and Its Role in Anomalous Afternoon Peak Precipitation. J Hydrometeorol, 541-556.
van den Hurk, B., F. Doblas-Reyes, G. Balsamo, R. D. Koster, S. I. Seneviratne, and H. Camargo, 2012: Soil moisture effects on seasonal temperature and precipitation forecast scores in Europe. Clim Dynam, 38, 349-362.
