Kirsten Findell (GFDL/NOAA) and Joe Santanello (NASA) Alpine Summer School on Land-Atmosphere Interactions Valsavarenche, Italy June 30, 2015

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1 Kirsten Findell (GFDL/NOAA) and Joe Santanello (NASA) Alpine Summer School on Land-Atmosphere Interactions Valsavarenche, Italy June 30,

2 Outline Diurnal Land-Atmosphere (L-A) Processes Local Coupling (LoCo) perspective and initiatives Metrics of L-A Coupling Mixing Diagram Approach Atmospheric controls on L-A interactions: CTP-HI low framework (Convective Triggering Potential, Low-level Humidity Index) Triggering/Amplification Feedback Strengths Observational Challenges to Diagnosing L-A coupling The need for improved PBL measurements Dataset length 2

3 Diurnal Cycle of the Boundary Layer From Stull, 1988 and by entrainment of air from the residual layer and the free atmosphere Sunrise: begins breakdown of nocturnal layer and growth of the BL Rate of growth impacted by fluxes from the land surface 3

4 Complexity of L-A Interactions Ek and Holtslag,

5 Is there a feedback (positive or negative) between soil moisture and precipitation? Difficult to assess given complex interactions and positive/negative feedbacks! Soil/Land surface Moisture Precipitation? Surface fluxes (radiative, turbulent) - Precipitation replenishes soil moisture - soil moisture controls surface flux partitioning - surface flux partitioning impacts PBL growth - PBL growth impacts rainfall triggering. The atmospheric branch is arguably the most uncertain: how surface fluxes influence PBL growth and how that growth influences convection. Which is more effective at triggering convection: PBL moistening (via more le) or enhanced lifting (via more H)?

6 Global Land-Atmosphere Coupling Experiment (GLACE) Koster et al. (2004) Multi-model GCM experiment to help understand the impact of soil moisture on precipitation. Sets of model experiments with and without interactions between the land surface and the atmosphere. Hot Spots of L-A coupling strength. These results have steered L-A investigations over the last decade more than any other study to date. Part of the GEWEX Global Land-Atmosphere System Study (GLASS) At that time, there was no coordinated effort to understand coupled landatmosphere systems at the local scale Local Coupling (LoCo) initiative fills important need

7 LoCo History Driving questions of LoCo: Are the results of previous offline efforts with land surface models (PILPS, GSWP) affected by the lack of L-A coupling? Can we explain the physical mechanisms leading to the coupling strength differences found in GLACE (SM-P) or other coupled NWP/climate experiments? Is there an observable diagnostic that quantifies the role of local land-atmosphere coupling? Challenges of LoCo: Land-atmosphere coupling takes place at many different spatial and temporal scales and involves many physical processes simultaneously. These multi-scale and multi-process phenomenon makes a proper definition of local L-A coupling not easy.

8 LoCo Process Chain Diagnose the components of GLACE at the diurnal process level: d( P) d( SM ) d( EF ) d( P) d( SM ) d( EF ) Soil moisture/vegetation determines surface fluxes and, thus, evaporative fraction Surface fluxes influence boundary layer growth BL growth rate influences entrainment Characteristics of free atmosphere air/entrainment influence the BL properties and development of clouds and precipitation LoCo DSM DEF DPBL DENT DT 2m,Q 2m DP/Clouds Process Chain (a) (b) (c) (d) SM P? H, le Santanello et al

9 LoCo Diagnostics Condensed list of LoCo metrics Each attempts to quantify particular links in the process-chain Range from simple correlations to multi-variate parameter space to preconditioning assessments Wide ranging input requirements (temporally, spatially) and model applications (SCM, MM, GCMs) Ultimate impact of land/near-surface variables on the PBL, clouds, precip Triggering Feedback Strength (TFS, Findell et al. 2011): statistical relationship between morning EF and afternoon precip Courtesy C. Ferguson

10 LoCo Diagnostics, Part 1: Mixing Diagram Approach Santanello et al., 2009, 2011, 2013 Inspired by Alan Betts FIFE papers. 10

11 Diurnal evolution of the boundary layer From a representative day in June 2002 at the SGP site BL deepens until ~3 hours after peak in H sfc q 2m increases steadily while the BL is deepening q 2m increases rapidly at first, then decreases with entrainment of dry air from above q q H sfc PBL h 7 am 7 pm 7 am 7 pm 11

12 Diurnal evolution of the boundary layer We can see this progression on mixing diagrams: the co-evolution of q 2m and q 2m in energy space Conserved variables Horizontal axis: proportional to moisture c p Dq a heat fluxes fluxes LDq a moisture fluxes 7pm Vertical axis: proportional to heat fluxes Require observations of the diurnal evolution of q and q Moistening Based on Betts (1984, 7am and warming for ~5 hours 1992) mixing diagram (vector) theory evaluated during FIFE. 12 Drying and warming for ~4 hours

13 Mixing Diagram Construction For a given Dt, we can measure BL height, H sfc, and le sfc From this we calculate the vector representing the surface terms From the residual vector, we can determine Dq ent, Dq ent, and b ent ; these are typically very hard to measure! c p Dq a heat fluxes LDq a moisture fluxes 7pm c p Dq ent 7am c p Dq sfc V ent LDq ent V sfc LDq sfc H sfc Dt PBLH :slope = b ent :slope = b sfc c p Dq sfc

14 Mixing Diagram Metrics b sfc = H sfc /LE sfc Surface Bowen Ratio -The partitioning of fluxes at the land surface (strong function of soil moisture). b ent = H ent /LE ent Entrainment Bowen Ratio -The amount of heat vs. dry air entrained into the PBL (function of gradient w/ free atmos.) A h = H ent /H sfc General Coupling Statistic -The entrainment rate produced (PBL) as a consequence of H sfc (LSM). -Otherwise known as the entrainment parameter. A le = LE ent /LE sfc Dry Air Entrainment Ratio -Quantifies the degree to which dry air entrainment offsets surface evaporation. -If ~ -1 then entrainment balances evaporation.

15 Mixing Diagram Construction We don t have to assume that the residual vector is all entrainment Advection can be estimated from model output, but difficult to apply to observations Santanello et al. (2013, 2015) now refer to V adv +V ent combined as the atmospheric response vector (V atm ) Can also include any other minimal (radiative flux divergence) contributions C p q (J/kg) 7pm 7am V ent C p Dq adv V sfc L q (J/kg) V adv LDq adv Dry, warm advection Heat/Moisture Budget in the PBL = Surface Flux + Entrainment + Advection

16 c p *T(J/kg) Mixing Diagrams 324,000 Model Range 40.5 N 7pm Entrainment Fluxes Observations 33.5 N Dry Wet 105 W Soil Moisture (m 3 /m 3 ) 94 W Fig. 1: Near-surface soil moisture map of the Southern Great Plains as simulated by LIS-WRF. 291,000 Dry Soils Dry Soils 7am Vector length = Flux Vector slope = Bowen Ratio Sfc Fluxes 7pm 7am Wet Soils 10,000 60,000 L*q(J/kg) Fig. 2: Daytime evolution of specific humidity vs. potential temperature for the dry and wet soil moisture locations in Fig. 1 Soil moisture differences lead to significantly different signatures of heat and moisture evolution. The sensitivity of the L-A coupling is thus reflected in the balance between PBL and surface fluxes. This model gets the general behavior of PBL growth over wet vs dry soils, and timing of entrainment. Method can be used to determine model deficiencies. Santanello et al., 2009

17 c p *T(J/kg) c p *T(J/kg) Sample Intercomparison Results: Conserved thermodynamic quantities can be overlain YSU MYJ MRF OBS Noah Site E4 TESSEL Site E4 CLM Site E4 b ent b sfc 320,000 YSU MYJ MRF OBS YSU MYJ MRF OBS A LE A H L*q(J/kg) 296,000 10,000 L*q(J/kg) 50,000 L*q(J/kg) Statistics based on evolution of T2m and Q2m vs. observations, derived from the mixing diagrams above. Noah+ YSU Noah+ MYJ Noah+ MRF TESS+ YSU TESS+ MYJ TESS+ MRF CLM+ YSU CLM+ MYJ CLM+ MRF RMSE T Q BIAS T Q Total Energy

18 LoCo Diagnostics, Part 2: Atmospheric Controls on L-A Interactions: The CTP-HIlow Framework Formulation of the idea that the early-morning atmosphere determines whether or not the land surface can influence the development of convection The most effective means of triggering convection is dependent on the low-level atmosphere We know the BL is going to grow into and entrain some of the overlying early-morning atmosphere We want some way to assess the air likely to be entrained: The stability The humidity (or humidity deficit) 18

19 The CTP-HIlow Framework Why do we want to assess the stability? Let s think of convection being triggered when the BL top meets the level of free convection (LFC) Then BL growth or LFC fall (or some combination of the two) can lead to convection LFC fall is dependent on temperature lapse rate LFC falls faster when T profile is closer to moist adiabatic Dry soils High H; Low E Deep BL Low Q E in BL BL h grows up to relatively stable LFC Wet soils Low H; High E Shallow BL High Q E in BL LFC falls to slowly growing h 19

20 The CTP-HIlow Framework Stability measure: CTP = the area between a moist adiabat and the observed temperature, in the region between Humidity deficit measure: HI low = sum of dew point depression 50 and 150 mb AGS 100 and 300 mb AGS HI low = T 950 T d,950 + T 850 T d,850 Findell and Eltahir, 2003a Rarely incorporated into BL Critical region: lapse rate important Usually incorporated into BL Results are not sensitive to the particulars of the humidity measure! Temperature structure and humidity deficit both matter 20

21 Wet soil advantage example LFC LFC LCL h LCL h Close to moist adiabatic; Low CTP 1D modeling work using a version of Kim and Entekhabi s (1998a,b) model of SEB and PBL; observed initial soundings over very wet and very dry soils Convection is favored over wet soils Wet soils Dry soils Local time (hours) 21

22 Dry soil advantage example LFC LFC LCL h LCL h Close to dry adiabatic; High CTP Convection is favored over dry soils Wet soils Dry soils 22

23 The CTP-HIlow Framework Sometimes it is easier to trigger rain over wet soils, sometimes over dry soils Different locations have different typical early-morning atmospheric conditions with different predispositions for triggering 23

24 Atmospherically controlled regions: more than 80% of days in atmospherically controlled parts of CTP-Hilow space (AC) Positive feedback regions: ~40-70% of days AC; Remaining days predominantly wet soil advantage Negative feedback region: ~75% of days AC; Remaining days most often dry soil advantage Transitional regions: ~65-80% of days AC; Remaining days ~equally distributed Findell and Eltahir,

25 Extensions of the CTP-HIlow Framework Changes in thresholds in CTP-HIlow space Tuinenburg et al. (2011, JClim) over India Ferguson and Wood (2011, JHM): satellite sources either have biases and/or require different threshold values in CTP-HI low parameter space Roundy et al. (2013, JHM): Can determine if a given day is wet or dry-soil advantage. Useful for understanding things in realtime. Hua Su et al. (2013, JHM): colder climates/earlier times of year require focus on slightly different atmospheric region Findell and Elthair (2003c) on wind effects: Backing winds or strongly sheared unidirectional winds suppress convection Veering winds with little to moderate shear are most favorable to convection 25

26 LoCo Diagnostics, Part 3: Dependence of Afternoon Rainfall on Evaporative Fraction We wanted to extend the CTP-HI low framework by including some information about the land surface EF = le/(h+le) captures flux partitioning without requiring soil moisture data Data show there is some relationship between precip and EF. Considering a simplified local view Only includes the connection between surface fluxes and precip Atmosphere Land From Dirmeyer, 2006 P W1 W2 ET 26

27 Dependence of Afternoon Rainfall on Evaporative Fraction Applied to North American Regional Reanalysis (NARR) data (Mesinger et al., 2006) 25 years ( ), 3 hourly, 1/3 degree Assimilated hourly precipitation Land surface is modeled data For a 1 1 box (Max of 25 yrs*92 JJA days*9 grid points = total days included) Colors represent afternoon rain on days with no rain in the morning HI low Near Memphis, TN Low EF Moderate EF High EF CTP High spatio-temporal resolution; Good approximation to reality + internally physically consistent in terms of surface fluxes and precipitation 27

28 HI low Portland, OR OR Low EF Moderate EF High EF CTP NARR data: 25 years, days in JJA 9 points in each 1 1 box (Max of 25*92*9 = points) Colors represent afternoon rain on days with no rain in the morning Philadelphia, PA Philadelphia, PA SW New Mexico SW New Mexico Central Mexico Central Mexico 28

29 Triggering/Amplification Feedback Strengths We want to assess the impact of morning surface evaporative fraction (EF) on the frequency and intensity of afternoon convective rainfall (r) ( rain) [ rain] and Ideally we only consider EF EF days when the local perspective is appropriate Morning EF = LE LE+H.. r = afternoon rainfall 9 am - noon noon - 6 pm Days with morning rain are excluded Days with CTP < 0 are excluded 29

30 Increasing Humidity Deficit Prob(rain) [C] Dependence of Afternoon Rainfall on Evaporative Fraction 17N, 96W (SE of Mexico City) 100< CTP < < HIlow < 10 Increasing CTP [J/kg] EF Color of bars indicates percentage of days falling in that CTP-HIlow bin > 15% 5 to 15%.5 to 5% 30

31 Dependence of Afternoon Rainfall on Evaporative Fraction Treat before-noon EF (Z or z), CTP (x), HI low (y), and afternoon rain as discrete random variables with prescribed bins in the CTP-HI low -EF parameter space For each CTP-HI-EF triple, we can determine the conditional probability of afternoon rainfall: ( rain CTP x, HI y, EF ) And we can obtain: max xmax ymax ( rain) 1 x 1 y 1 ( rain x, y, ) ( x, y ) ( ) 31

32 Dependence of Afternoon Rainfall on Evaporative Fraction Triggering Feedback Strength (in probability units): TFS = σ EF Γ(r) EF High evaporation enhances the probability of afternoon rainfall east of the Mississippi and in Mexico. Variations in surface fluxes lead to 10-25% change in afternoon rainfall probability in these regions. Amplification Feedback Strength (in mm): AFS Ε[r] AFS = σ EF EF The intensity of rainfall is largely insensitive to surface fluxes. 32 TFS Findell et al., 2011

33 normtfs Number of Observations/bin normafs Number of Observations/bin Functional relationships Normalized: normtfs = EF Γ(r) Γ(r) EF normtfs vs mean EF normafs = EF Ε[r] Ε[r] EF normafs vs mean EF PDF of EF Mean EF Mean EF Triggering sensitivity is positive throughout Hints of negative possibilities when EF < 0.2 Highest in humid regimes; Peaks at ~0.8? Amplification sensitivity: noisy, centered on 0.0 Findell et al.,

34 Dependence of Afternoon Rainfall on EF GFDL s AM2.1 shows a similar (slightly weaker) positive feedback between high before-noon EF and afternoon rainfall in JJA in some regions But it seems they are getting the same answer for different reasons TFS NARR s EF NARR TFS AM2.1 s EF AM2.1 Berg et al., 2013 Sensitivity term Sensitivity term 34

35 Summary: Return to the LoCo Process Chain Mixing Diagrams: allow for separation and quantification of portions of the LoCo Process Chain CTP-HI low Framework: focused on how DENT constrains the possible impact of DSM and DEF on DP Triggering is sometimes easier through BL growth, sometimes through BL moistening Triggering/Amplification Feedback Strength: a statistical assessment of the net, long term sensitivity of DP to DEF LoCo DSM DEF DPBL DENT DT 2m,Q 2m DP/Clouds Process Chain (a) (b) (c) (d) Santanello et al

36 Spatial Scale Current Challenges Heterogeneity and Representativeness (inc. soils, vegetation) Local vs. Non-local Coupling Advection/Large-Scale Processes Temporal Scale Metric requirements vary significantly Model applications range from local/point/diurnal to global/climate Models & Diagnostics True coupling strength metric is elusive and transient Disparate scales, but should be complimentary approaches E.g. GLACE & TFS results (GCM world) could be explained by Terrestrial and PBL links in the chain metrics. Diagnostics and models, including the land and PBL schemes within, ultimately need to be evaluated and applied at both the local and global scales. 36

37 Satellite Monitoring of L-A Interactions Satellite Data Not yet benchmark quality - fluxes (ET), soil moisture, PBL all limited Treat them as another model estimate and determine future requirements/improvements Global Monitoring of LoCo Variables from Satellite: Land Surface (well-established) Soil Moisture: Microwave (AMSR-E, SMOS, SMAP) Evaporation: Thermal/IR, MODIS, Microwave Global ET efforts underway (e.g. Landflux-Eval) Includes LST, Veg, Soils PBL (under established) Mixed-layer T/q profiles and evolution: IR Sounders (AIRS, IASI) LCL: GOES, IR Sounders (AIRS, IASI) PBL Height: AIRS, CALIPSO, GPS-RO.? Need future mission dedicated to PBL retrieval over land!

38 Importance of long observational records A sampling exercise with NARR data to demonstrate the variability that can come with very short samples For many different record lengths: 300 bootstrap samples of precip and EF used to calculate stfs 92 day samples (one summer long): the range of stfs estimates is huge, the mean not clearly defined As sample length increases, the bootstrap estimates converge on a clearer mean value Says that if you want to trust the estimate derived from a single observational record, it must be a LONG record! 92-day samples 552-day samples 2300-day samples 38

39 Best estimate Best estimate Importance of long observational records The data length requirement is particularly important for derived variables: they need longer records than directly observed variables X-axis: 300 bootstrap values from each station Y-axis value: best estimate of true value at that station (mean of all bootstraps from the longest samples) Inner 50% Range Findell et al., 2015 Sample length: 92 days 276 days 552 days 920 days 1380 days 2300 days 1 summer 3 summers 6 summers 10 summers 15 summers 25 summers mean daily precip [mm/day] Values calculated from each bootstrap at each of 126 stations TwoLegILH [kg/m2] Median Outliers 39

40 Best estimate Best estimate Importance of long observational records And if your obs have noise, derived metrics show a unidirectional reduction of metric strength that doesn t go away. Sample length: 92 days 276 days 552 days 920 days 1380 days 2300 days 1 summer 3 summers 6 summers 10 summers 15 summers 25 summers mean daily precip [mm/day] TwoLegILH [kg/m2] Findell et al., 2015 Bootstrap estimates 40

41 Thank you! 41