On the Limitations of Satellite Passive Measurements for Climate Process Studies Steve Cooper 1, Jay Mace 1, Tristan L Ecuyer 2, Matthew Lebsock 3 1 University of Utah, Atmospheric Sciences 2 University of Wisconsin, Atmospheric and Oceanic Sciences 3 Jet Propulsion Lab, Pasadena
Motivation Clouds and precipitation play an important role in the regulation of climate and in climate change scenarios Satellites provide an ideal means to monitor global properties of clouds and precipitation from both active sensors (e.g. radar and lidar) and passive sensors (e.g. microwave, visible, near-infrared, infrared)
Motivation NASA has no plans to replace dying active sensors such as the CloudSat Cloud Profiling radar until late 2020s at the earliest Talk explores the scientific implications of such decisions in context of recent state-of-the-art climate studies
Outline 1. Brief overview of specific cloud and precipitation scenarios that are important for our understanding of climate: focus on marine stratocumulus water clouds, ice clouds, and high-latitude mixed-phase clouds 2. Examine the physics (sensitivities and uncertainties) of cloud and precipitation retrievals from passive measurements for cloud scenarios given in Point 1. 3. Briefly contrast potential of passive retrievals with those from both active (radar-lidar) retrievals and combined active-passive retrieval schemes.
Overview - clouds are important in climate Ø Clouds reflect solar energy, absorb infrared energy à determine warm or cool? Ø Latent heat from precipitation is a BIG source of energy to the atmosphere Ø Clouds are largely a second order effect in many ways but drive our uncertainty in understanding climate
Cloud Climate Feedback Ø The vertical distribution of these impacts play a role in cloud climate feedback processes involving sea surface temperature, convection, water vapor, and dynamics Ø Cloud feedbacks are poorly understood
Everything else Clouds IPCC AR4: Cloud Feedbacks are the major source of climate change uncertainty - due both to warming and global precipitation changes. Cloud-related feedback processes dominate these uncertainties. From Dufresne and Bony (2008)
From where does the model variability in cloud feedback arise? Mixed-phase Cu Deep convection Sc (Soden and Vecchi 2011) Subtropical low cloud (Sc, Cu) and Arctic mixed phase cloud are the principal sources of model variability.
Yet, tropical cirrus are the primary source of the positive cloud feedback But NOT the uncertainty! Need a better understanding of stratocumulus and high-latitude mixed phase cloud properties and processes in terms of models
The Link between modeling and understanding is represented in the parameterization of processes and their effects in models
Ø We need to define vertical profiles of particle distributions (aerosol, cloud, precip) from remote sensors if some understanding of process is to emerge Ø Better define real world Ø Model parameterization schemes à own challenge Ship tracks Drizzle
So, can we solve the cloud feedback problem? Existing measurements provide an excellent foundation from which to build but we have made amazingly little progress over the years. Why? Two Fundamental Reasons... 1. Non-uniqueness: the problem has been (and largely remains) severely under-constrained with existing data sets. 2. Lack of sensitivity: the majority of the condensate in the atmosphere is hidden from most of our sensors
Primary example: can we characterize marine stratocumulus? Consider visible, near-infrared, and microwave measurements Can we determine basic cloud properties given available passive instrumentation, e.g. are the clouds drizzling? Rainfall and its intensity affect organization and duration of these cells which effect cloud forcing, etc
MODIS visible, near-infrared retrievals of particle size: Physics Cloud optical depth and particle size can be retrieved from the 0.66 µm visible and 2.15 µm reflectances, respectively Typical cloud drops are ~ 15 µm Typical drizzle drops are ~ 50 µm!
Apply MODIS to cloud & drizzle scenarios Cloud Only 6:1 Cloud to Drizzle ratio by mass 2:1 Cloud to Drizzle ratio by mass τ =20 R eff = 16µm DSD = 100 µm From a MODIS perspective, we see little difference for these cases τ =20.0 R eff = 16.0 µm τ =20.5 R eff = 16.5 µm τ =21.6 R eff = 16.8 µm From satellite measurement, no way to tell difference between cloud and rain
Limitations of MODIS (visible, near-ir) technique Very little sensitivity to drizzle or rain water Red and Blue diamonds correspond to addition of drizzle with different vertical distributions to cloud Results even for idealized retrieval scenario, i.e. we know PSD, vertical structure and have no horizontal inhomogeneities (no 3-D radiative transfer) These retrievals are really only valid for pure cloud scenarios, MODIS offers little hope in characterizing msc clouds
Compare MODIS Observations in context of CloudSat observations of rainfall! PDF of MODIS retrieved effective radius using alternate near-infrared 2.1 m and 3.7 m measurements. Precipitating and nonprecipitating scenes were determined from CloudSat CPR measurements, as taken from Lebsock et al. (2011).
Marine Stratocumulus- Passive Microwave Ø Passive microwave observations from sensors such as AMSRE have long been used to estimate global rainfall (19 and 37 GHz) Ø Microwave signal, however, is a function of both cloud and rainwater. These algorithms therefore depend upon an A PRIORI knowledge of the partition of cloud and rain as a function of LWP for final estimate of cloud rainwater Ø No confidence for a given retrieval Ø Get an estimate of rainfall but you can tweak algorithm to get your desired answer. And CANNOT capture changes in cloud rain partition for given LWP with climate feedbacks, e.g. aerosol effects
Lebsock et al. (2011) Detecting the Ratio of Rain and Cloud Water in Low-Latitude Shallow Marine Clouds Ø Use a combined CloudSat- MODIS retrieval as a guide à CloudSat has sensitivity to precipitation droplets and MODIS to cloud droplets Figure shows cloud water plotted versus precipitating water Study finds weakly precipitating systems have at most a 2:1 cloud to drizzle water ratio FIG. 6. Estimates of the mean relationship between the cloud water path W c and the precipitation water path W p given two assumptions regarding the precipitation DSD. The parameters that describe the DSD are described in the text. The circles and error bars respectively show the median value and the range of 75% of the data. The dashed lines show the ratio W p :W c for values of 1:1, 1:2, and 0:1, as indicated on the right axis.
Combined MODIS- AMSRE scheme to detect rain We developed our own combined approach that takes advantage of MODIS sensitivity to cloud water and AMSRE sensitivity to rain water For our example, the only source of error is cloud LWP as derived from MODIS observations. W cloud '=420'g/m 2 '' W precip' ='0'g/m 2 '' W cloud '=320'g/m 2 '' W precip' ='80'g/m 2 '' T b,'19'ghz '='161.5'±'0.5K' ' T b,'37'ghz '='194.0'±'0.5K' W cloud '=180'g/m 2 '' W precip' ='160'g/m 2 '' Nearly identical microwave brightness temperatures (within 1K- calibration error) are found across the realistic range of expected cloud and precipitation property scenarios for these shallow marine systems.
Non uniqueness of AMSRE- MODIS for drizzle! Simulated 19 GHZ and 37 GHz brightness temperatures. The grey pixels represent those cloud and rain water path scenarios that have the same radiometric signature within 0.5K as a base state with cloud LWP of 320 g/m 2 and drizzle path of 80 g/m 2.
Non-uniqueness of AMSRE- MODIS for drizzle! Simulated 37 GHZ observations. The grey pixels in the left and right panels represent those cloud and drizzle water path scenarios that have the same radiometric signature within expected calibration and water vapor profile uncertainties, respectively, as a base state with cloud LWP of 320 g/m 2 and drizzle path of 80 g/m 2.
Combined MODIS - AMSRE precipitation scheme Ø A combined AMSRE- MODIS is unable to discriminate between precipitating and non-precipitating shallow marine clouds for many scenes due to inherent non-uniqueness issues resulting from known uncertainties for each instrument. Ø This conclusion holds for a near perfect retrieval scheme is which we overcome footprint matching issues between MODIS and AMSRE and are correct in all microwave retrieval assumptions (atmospheric profile, surface emissivity, DSD, etc.) Ø Get around the limitations of passive techniques by using a combination of active and passive sensors, CloudSat profiling radar has sensitivity to the vertical profile of drizzle and MODIS has sensitivity to cloud drops
Lebsock et al. (2011) Detecting the Ratio of Rain and Cloud Water in Low-Latitude Shallow Marine Clouds Ø This is the first large-scale observational estimate of this relationship known to the authors and could provide some bounds on the assumptions that are imposed a priori in microwave remote sensing retrievals of precipitation rate Figure shows cloud water plotted versus precipitating water This approach has errors (20-50% for precip) as well due to such assumptions as shape of drop size distribution Confident of precip due to first order sensitivity, a start FIG. 6. Estimates of the mean relationship between the cloud water path W c and the precipitation water path W p given two assumptions regarding the precipitation DSD. The parameters that describe the DSD are described in the text. The circles and error bars respectively show the median value and the range of 75% of the data. The dashed lines show the ratio W p :W c for values of 1:1, 1:2, and 0:1, as indicated on the right axis.
0 Example 2: Partition of Cloud and Precipitating Ice This ratio is important in climate models. Waliser et al. (2008,2011) points out there is a fundamental disconnect between remote sensing efforts and model treatment of ice species. 100 200 55 Cloud Ice Precip ratio djf 1330 55 70 55 55 100 80 These ratios impact such model fields as precipitation intensity, duration, and distribution through their influence on model parameterization schemes. Pressure [hpa] 300 400 500 600 700 40 25 25 10 40 5 0 25 10 5 40 0 10 25 40 55 25 60 40 20 A lack of real-world knowledge of these ratios for constraint places some doubt on the veracity of both the parameterization schemes and simulation results. 800 10 900 20 5 10 1000 5 20 80 60 40 20 0 20 40 60 80 Latitude 0 0 %
Vertical characterization of cloud ice and precipitating ice distributions Ice clouds are important in climate yet properties are elusive. Can we use MODIS visible, near-infrared approach for homogenous clouds?!
Non-Uniqueness issues for homogenous thin cirrus! Simulated 2.15 m and 0.66 m reflectances (unitless). The gray pixels represent the combinations of cloud effective radii and optical depth that have the same radiometric signature as cloud state with an effective radius of 20 m and an optical depth of 1.0 given a 2% uncertainty in sea surface albedo.
Non-Uniqueness issues for homogenous thick cirrus Simulated 2.15 m and 0.66 m reflectance (unitless) calculations. The gray pixels represent the combinations of cloud effective radii and optical depth that have the same radiometric signature as cloud state with an effective radius of 28 m and an optical depth of 15 given 20% uncertainty in reflectances.
Vertical characterization of cloud ice and precipitating ice distributions for vertically inhomogenous clouds A simple retrieval scenario in which an ice cloud is composed of two distinct homogenous cloud layers. The value above each cloud scene represents the retrieved effective radius as seen from the perspective of a space-borne sensor such as MODIS. 50"μm" "" 28.5"μm" "" 19"μm" "" 12.2"μm" "" τ"="2.0,"r eff "="12"μm" τ"="4.0,"r eff "="12"μm" τ"="10,"r eff "="12"μm" τ"="25" R eff "="50"μm" τ"="25" R eff "="50"μm" τ"="25" R eff "="50"μm" τ"="25" R eff "="50"μm" Even for simple two-layer cloud, problem is wildly non-unique, i.e. need to know answer to get answer
0 Partition of Cloud and Precipitating Ice How retrieve complex vertical ratio? 100 Cloud Ice Precip ratio djf 1330 100 70 55 80 Pressure [hpa] 200 300 55 40 55 25 40 55 55 60 Hope with sub mm microwave instruments but may need radar for profile quantification 400 500 600 700 40 25 25 10 5 0 10 5 0 10 25 40 25 40 20 Area of active study for design of future satellite missions 800 10 900 20 5 10 1000 5 20 80 60 40 20 0 20 40 60 80 Latitude 0 0 %
CloudSat observations of snowfall- Wood and L Ecuyer (UW) CloudSat and CPR Parameters 94 GHz (3.2 mm, W-band) Inclination: 98 degrees Vertical resolution: 485 m Footprint: 1.7 x 1.4 km Sensitivity: -28 dbz
Difficulties with active sensors, A-Train Holds for CloudSat snowfall retrievals Marine Stratocumulus water clouds in previous example Ultimate goal is to figure path forward
Mini-Example: High Latitude- Mixed Phase Clouds Light Precipitation Validation Experiment (LPVEx): Ø In reality, we need to characterize cloud/ precipitation scenarios much more sophisticated than simple water and ice clouds Ø Active measurements such as the advanced radars flown during LPVEx provide a possible way forward for a better understanding of climate. Ice Rain Snow
Combined Active- Passive Retrieval Schemes Active sensors allow us to bypass some lack of sensitivity and non-uniqueness issues inherent to passive Radar and lidar observations have complimentary sensitivities to vertical cloud profile Active can be used to constrain non-uniqueness issues from passive or vice versa, if selected wisely
Our current research focuses on combined active- passive retrieval schemes from available instrumentation with application to climate problems NASA A-Train Constellation Next-Generation ACE Mission (precip and clouds) We want spaceborne multi-frequency radar with Doppler capabilities combined with traditional passive sensors Sub-mm combined with dual frequency radars!
Conclusions Ø Passive remote sensing measurements from satellites (visible, infrared, microwave) undeniably provide critical information for both the prediction of weather and characterization of long-term climate change Ø Our work suggests that passive measurements alone, however, cannot confidently discriminate between some key cloud and precipitation states Ø We therefore cannot answer seemingly simple but climatologically important questions (e.g. are shallow marine clouds drizzling or not? Are cloud ice particles big or small?) Ø We need the continued employment and advancement of space-borne active measurements used in co-incidence with passive measurements, when possible, as we work towards an improved understanding of climate.
Extra Slides
W-band reflectivity vs snowfall rate Variability in snow particle mass, shape, size distribution uncertainty in the observed relation between Ze and S (green points): Broad distribution Narrow distribution m(d) = ad b
CloudSat W-band Snowfall Retrieval " " Incorporates a priori information about snow microphysical properties and size distributions Developed from analysis of surface and aircraft observations of snow Microphysical parameters m(d) = αdβ Ap(D) = γdσ Values of α, β, γ, σ used to constrain discrete dipole models for radar scattering properties
CloudSat W-band Snowfall Retrieval " Provides profiles of microphysical properties and snowfall rates, and estimates of surface snowfall over land and ocean with rigorous uncertainty estimates Retrieval-derived Ze-S (black) and uncertainties (gray) capture variability in observed Ze-S (earlier slide)
CloudSat W-band Snowfall Retrieval " Some validation results Retrieval applied to ground-based W-band observations give accumulations consistent with gauge observations Favorable comparisons obtained between aircraft observations and ~coincident CloudSat-retrieved size distribution parameters. Longer-term regional results will be used for e.g., basin-scale evaluations: Mean retrieved snowfall rates for the Arctic, DJF 2006-2007
Physical Basis for Cloud Retrievals Ø Optical properties of ice crystals Ø Atmospheric temperature and gases profile
Example Passive Ice Cloud Retrieval Schemes Platt et al. (1980) Szejwach (1982) Prabhakara (1988) Nakajima and King (1990) Liou et al. (1990) Stone et al. (1990) Wielicki et al. (1990) Ou et al. (1995) Arking and Childs (1985) Twomey and Cocks (1989) Gao and Kaufman (1995) Smith et al. (1996) CSU 10, 12 µm 6.5, 11.5 10.6, 12.8 0.65, 2.13 6.5, 10.6 3.7, 10.9, 12.7 0.83, 1.65, 2.21 3.7, 10.9 0.65, 3.7, 11.0 0.75, 1.0, 1.2, 1.6, 2.25 0.65, 1.37 3.9, 10.7, 12.0 0.65, 2.13, 4.05, 11.0, 13.3
Split- Window Retrieval Scheme Ø Based upon spectral variation of absorption by ice cloud particles across the window region Ø Sensitivity to limited range of retrieved cloud properties Ø Requires accurate cloud boundary information
Retrieval Scheme Discontinuities Consistency between retrieval schemes and across different measurement campaigns is desirable
Optimal- Estimation Framework
Estimation of Sy Matrix Assumptions of size distribution and ice crystal habit affect radiative transfer calculations
Split- Window Retrieval Results Retrieved optical depth and effective radius are dependent upon uncertainties in both the cloud temperature (i.e. observation system) and the forward model assumptions such as ice crystal habit