Stephen S. Leroy

Achieving Climate Change Absolute Accuracy in Orbit

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREOs inherently high absolute accuracy will be verified and traceable on orbit to Systeme Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earths thermal infrared spectrum (5–50 μm), the spectrum of solar radiation reflected by the Earth and its atmosphere (320–2300 nm), and radio occultation refractivity from which...

Climate Signal Detection Times and Constraints on Climate Benchmark Accuracy Requirements

Long-term trends in the climate system are always partly obscured by naturally occurring interannual variability. All else being equal, the larger the natural variability, the less precisely one can estimate a trend in a time series of data. Measurement uncertainty, though, also obscures long-term trends. The way in which measurement uncertainty and natural interannual variability interact in inhibiting the detection of climate trends using simple linear regression is derived and the manner in which the interaction between the two can be used to formulate accuracy requirements for satellite climate benchmark missions is shown. It is found that measurement uncertainty increases detection times, but only when considered in direct proportion to natural variability. It is also found that detection times depend critically on the correlation time of natural variability and satellite lifetime. As a consequence, requirements on satellite climate benchmark accuracy and mission lifetime must be directly related to the natural variability of the climate system and its associated correlation times.

Detecting Climate Signals: Some Bayesian Aspects

Abstract A Bayesian approach to detecting forced climate signals in a dataset is presented. First, the detection algorithm derived is shown to be capable of uniquely identifying several signals optimally. Other detection techniques are shown to be limiting cases. Second, this approach naturally lends itself to rating models relatively according to their predictions. Both the accuracy of the model prediction and the precision of the prediction are accounted for in rating models. In general, complex models are less probable than simpler models. Finally, this approach to detection is used to detect a signal induced by the solar cycle in the surface temperature record over the past 100 yr. The solar cycle signal-to-noise ratio is found to be ∼1 but is probably not detected. Estimates of the natural variability noise are taken from model prescriptions, each of which is vastly different. The Geophysical Fluid Dynamics Laboratory models, though, best match the residual temperature fluctuations after the signals ...

Testing Climate Models Using Thermal Infrared Spectra

Abstract An approach to test climate models with observations is presented. In this approach, it is possible to directly observe the longwave feedbacks of the climate system in time series of annual average outgoing longwave spectra. Tropospheric temperature, stratospheric temperature, water vapor, and carbon dioxide have clear and distinctive signatures in the infrared spectrum, and it is possible to detect trends of these signals unambiguously from trends in the outgoing longwave spectrum by optimal detection techniques. This approach is applied to clear-sky data in the tropics simulated from the output of an ensemble of climate models. Estimates of the water vapor–longwave feedback by this approach agree to within estimated errors with truth, and it is likely that an uncertainty of 50% can be obtained in 20 yr of a continuous time series. The correlation of tropospheric temperature and water vapor anomalies can provide a constraint on the water vapor–longwave feedback to 5% uncertainty in 20 yr, or 7% ...

Climate Benchmarking Using GNSS Occultation

We put climate monitoring in a scientific context, which can be arrived at through a careful implementation of Bayesian inference. What we find is that a good climate monitoring tool must help address the physics of a climate model so as to make it better able to predict future climates. GNSS occultation is ideal because it offers sensitivity to improve the model physics which affects the stratospheric Brewer-Dobson circulation, the tropical tropospheric hydrological cycle, and the poleward migration of the mid-latitude storm track. Also, GNSS occultation is ideal because it can be readily made into a benchmark measurement provided clock calibration is always done by double-differencing, and measurements used to determine precise orbits and information on ionospheric activity are archived as auxiliary information. In doing so, GNSS occultation can be made S.I. traceable.

Formulation and validation of simulated data for the Atmospheric Infrared Sounder (AIRS)

Models for synthesizing radiance measurements by the Atmospheric Infrared Sounder (AIRS) are described. Synthetic radiances have been generated for developing and testing data processing algorithms. The radiances are calculated from geophysical states derived from weather forecasts and climatology using the AIRS rapid transmission algorithm. The data contain horizontal variability at the spatial resolution of AIRS from the surface and cloud fields. This is needed to test retrieval algorithms under partially cloudy conditions. The surface variability is added using vegetation and International Geosphere Biosphere Programme surface type maps, while cloud variability is added randomly. The radiances are spectrally averaged to create High Resolution Infrared Sounder (HIRS) data, and this is compared with actual HIRS2 data on the NOAA 14 satellite. The simulated data under-represent high-altitude equatorial cirrus clouds and have too much local variability. They agree in the mean to within 1-4 K, and global standard deviation agrees to better than 2 K. Simulated data have been a valuable tool for developing retrieval algorithms and studying error characteristics and will continue to be so after launch.

COSMIC Radio Occultation Processing: Cross-Center Comparison and Validation

AbstractA radio occultation data processing system (OCC) was developed for numerical weather prediction and climate benchmarking. The data processing algorithms use the well-established Fourier integral operator–based methods, which ensure a high accuracy of retrievals. The system as a whole, or in its parts, is currently used at the Global Navigation Satellite System Receiver for Atmospheric Sounding (GRAS) Satellite Application Facility at the Danish Meteorological Institute, German Weather Service, and Wegener Center for Climate and Global Change. A statistical comparison of the inversions of the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) data by the system herein, University Corporation for Atmospheric Research (UCAR) data products, and ECMWF analyses is presented. Forty days of 2007 and 2008 were processed (from 5 days in the middle of each season) for the comparison of OCC and ECMWF, and 20 days of April 2009 were processed for the comparison of OCC, UCAR, and EC...

Generating climate benchmark atmospheric soundings using GPS occultation data

Atmospheric soundings derived from Global Positioning System radio occultations (GPSRO) acquired in low-Earth orbit have the potential to be global climate benchmark observations of significant value to the Global Climate Observing System (GCOS). Geophysical observables such as atmospheric pressure and temperature are derived by measuring propagation delay induced by the atmosphere, a measurement whose fundamental unit-the second-is absolutely determined by calibration against atomic clocks. In this paper, we analyze the sources of systematic and random error for GPSRO soundings to determine the steps needed to establish GPSRO as a climate benchmark observation. Benchmarks require specific processing strategies and specific forms of documentation so that confidence in the accuracy and precision of the measurements is assured. Establishing calibration traceability to absolute standards (SI-traceability) is an essential strategy. We discuss a wide range of error sources in a geophysical retrieval, such as orbit determination error, signal delay in the Earths ionosphere, and quality control strategies. Uncalibrated ionospheric delay is identified as the error source deserving the most attention in establishing SI-traceability of the retrievals, to meet stringent climate observation requirements of 0.5 K accuracy and 0.04 K stability. Profile comparisons from the recently launched COSMIC constellation establish strong upper limits on systematic error arising from the individual instruments. These encouraging results suggest that GPSRO should become a permanent resource for the GCOS. These highly precise and accurate instruments can be deployed on future Earth Observation satellites at a low per-sensor cost and minimal interference to existing and planned observational programs.

The power of vertical geolocation of atmospheric profiles from GNSS radio occultation

Abstract High‐resolution measurements from Global Navigation Satellite System (GNSS) radio occultation (RO) provide atmospheric profiles with independent information on altitude and pressure. This unique property is of crucial advantage when analyzing atmospheric characteristics that require joint knowledge of altitude and pressure or other thermodynamic atmospheric variables. Here we introduce and demonstrate the utility of this independent information from RO and discuss the computation, uncertainty, and use of RO atmospheric profiles on isohypsic coordinates—mean sea level altitude and geopotential height—as well as on thermodynamic coordinates (pressure and potential temperature). Using geopotential height as vertical grid, we give information on errors of RO‐derived temperature, pressure, and potential temperature profiles and provide an empirical error model which accounts for seasonal and latitudinal variations. The observational uncertainty of individual temperature/pressure/potential temperature profiles is about 0.7 K/0.15%/1.4 K in the tropopause region. It gradually increases into the stratosphere and decreases toward the lower troposphere. This decrease is due to the increasing influence of background information. The total climatological error of mean atmospheric fields is, in general, dominated by the systematic error component. We use sampling error‐corrected climatological fields to demonstrate the power of having different and accurate vertical coordinates available. As examples we analyze characteristics of the location of the tropopause for geopotential height, pressure, and potential temperature coordinates as well as seasonal variations of the midlatitude jet stream core. This highlights the broad applicability of RO and the utility of its versatile vertical geolocation for investigating the vertical structure of the troposphere and stratosphere.

Determining Longwave Forcing and Feedback Using Infrared Spectra and GNSS Radio Occultation

Abstract The authors investigate whether combining a data type derived from radio occultation (RO) with the infrared spectral data in an optimal detection method improves the quantification of longwave radiative forcing and feedback. Signals derived from a doubled-CO2 experiment in a theoretical study are used. When the uncertainties in both data types are conservatively estimated, jointly detecting the feedbacks of tropospheric temperature and water vapor, stratospheric temperature, and high-level cloud from the two data types should reduce the mean errors by more than 50%. This improvement is achieved because the RO measurement helps disentangle the radiance signals that are ambiguous in the infrared spectrum. The result signifies the complementary information content in infrared spectral and radio occultation data types, which can be effectively combined in optimal detection to accurately quantify the longwave radiative forcing and feedback. The results herein show that the radiative forcing of CO2 and...