Cheng-Zhi Zou

The NCEP Climate Forecast System Reanalysis

The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global oceans latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice m...

Recalibration of microwave sounding unit for climate studies using simultaneous nadir overpasses

[1] The measurements from microwave sounding unit (MSU) on board different NOAA polar-orbiting satellites have been extensively used for detecting atmospheric temperature trend during the last several decades. However, temperature trends derived from these measurements are under significant debate, mostly caused by calibration errors. This study recalibrates the MSU channel 2 observations at level 0 using the postlaunch simultaneous nadir overpass (SNO) matchups and then provides a well-merged new MSU 1b data set for climate studies. The calibration algorithm consists of a dominant linear response of the MSU raw counts to the Earth-view radiance plus a smaller quadratic term. Uncertainties are represented by a constant offset and errors in the coefficient for the nonlinear quadratic term. A SNO matchup data set for nadir pixels with criteria of simultaneity of less than 100 s and within a ground distance of 111 km is generated for all overlaps of NOAA satellites. The simultaneous nature of these matchups eliminates the impact of orbital drifts on the calibration. A radiance error model for the SNO pairs is developed and then used to determine the offsets and nonlinear coefficients through regressions of the SNO matchups. It is found that the SNO matchups can accurately determine the differences of the offsets as well as the nonlinear coefficients between satellite pairs, thus providing a strong constraint to link calibration coefficients of different satellites together. However, SNO matchups alone cannot determine the absolute values of the coefficients because there is a high degree of colinearity between satellite SNO observations. Absolute values of calibration coefficients are obtained through sensitivity experiments, in which the percentage of variance in the brightness temperature difference time series that can be explained by the warm target temperatures of overlapping satellites is a function of the calibration coefficient. By minimizing these percentages of variance for overlapping observations, a new set of calibration coefficients is obtained from the SNO regressions. These new coefficients are significantly different from the prelaunch calibration values, but they result in bias-free SNO matchups and near-zero contaminations by the warm target temperatures in terms of the calibrated brightness temperature. Applying the new calibration coefficients to the Level 0 MSU observations, a well-merged MSU pentad data set is generated for climate trend studies. To avoid errors caused by small SNO samplings between NOAA 10 and 9, observations only from and after NOAA 10 are used. In addition, only ocean averages are investigated so that diurnal cycle effect can be ignored. The global ocean-averaged intersatellite biases for the pentad data set are between 0.05 and 0.1 K, which is an order of magnitude smaller than that obtained when using the unadjusted calibration algorithm. The ocean-only anomaly trend for the combined MSU channel 2 brightness temperature is found to be 0.198 K decade -1 during 1987-2003.

The mystery of recent stratospheric temperature trends

A new data set of middle- and upper-stratospheric temperatures based on reprocessing of satellite radiances provides a view of stratospheric climate change during the period 1979–2005 that is strikingly different from that provided by earlier data sets. The new data call into question our understanding of observed stratospheric temperature trends and our ability to test simulations of the stratospheric response to emissions of greenhouse gases and ozone-depleting substances. Here we highlight the important issues raised by the new data and suggest how the climate science community can resolve them.

Identifying human influences on atmospheric temperature.

We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.

Error Structure and Atmospheric Temperature Trends in Observations from the Microwave Sounding Unit

Abstract The Microwave Sounding Unit (MSU) onboard the National Oceanic and Atmospheric Administration polar-orbiting satellites measures the atmospheric temperature from the surface to the lower stratosphere under all weather conditions, excluding precipitation. Although designed primarily for monitoring weather processes, the MSU observations have been extensively used for detecting climate trends, and calibration errors are a major source of uncertainty. To reduce this uncertainty, an intercalibration method based on the simultaneous nadir overpass (SNO) matchups for the MSU instruments on satellites NOAA-10, -11, -12, and -14 was developed. Due to orbital geometry, the SNO matchups are confined to the polar regions, where the brightness temperature range is slightly smaller than the global range. Nevertheless, the resulting calibration coefficients are applied globally to the entire life cycle of an MSU satellite. Such intercalibration reduces intersatellite biases by an order of magnitude compared to...

Real-time weekly global green vegetation fraction derived from advanced very high resolution radiometer-based NOAA operational global vegetation index (GVI) system

[1] To provide quality-improved and consistent real-time global green vegetation fraction (GVF) data products that are suitable for use in operational numerical weather, climate, and hydrological models, necessary processing steps are applied to the output data stream from the advanced very high resolution radiometer (AVHRR)-based NOAA operational global vegetation index (GVI) system. This paper reviewed the NOAA GVI data and described the algorithm to derive weekly updated real-time GVF from the normalized difference vegetation index (NDVI). The methodology description focuses on algorithm justification in an operational production context. The described algorithm was implemented in the global vegetation processing system (GVPS). The new global GVF data sets include the multiyear GVF weekly climatology and the real-time weekly GVF. Compared to the old 5 year GVF monthly climatology currently used in the operational National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) weather and climate models, the new data sets provide an overall higher vegetation value, real-time surface vegetation information, and numerous other improvements. The new GVF data set quality was partially assured by validation against Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI at a few EOS land validation core sites and comparison with another independently processed NDVI data set. Impact of the new GVF data sets in numerical weather prediction (NWP) model was investigated using EMC mesoscale model simulations and concluded overall positive.

Construction of Stratospheric Temperature Data Records from Stratospheric Sounding Units

AbstractIn recognizing the importance of Stratospheric Sounding Unit (SSU) onboard historical NOAA polar-orbiting satellites in assessment of long-term stratospheric temperature changes and limitations in previous available SSU datasets, this study constructs a fully documented, publicly accessible, and well-merged SSU time series for climate change investigations. Focusing on methodologies, this study describes the details of data processing and bias corrections in the SSU observations for generating consistent stratospheric temperature data records, including 1) removal of the instrument gas leak effect in its CO2 cell; 2) correction of the atmospheric CO2 increase effect; 3) adjustment for different observation viewing angles; 4) removal of diurnal sampling biases due to satellite orbital drift; and 5) statistical merging of SSU observations from different satellites. After reprocessing, the stratospheric temperature records are composed of nadirlike, gridded brightness temperatures that correspond to ...

Stratospheric temperature changes during the satellite era

Satellite-based layer average stratospheric temperature (T) climate data records (CDRs) now span more than three decades and so can elucidate climate variability associated with processes on multiple time scales. We intercompare and analyze available published T CDRs covering at least two decades, with a focus on Stratospheric Sounding Unit (SSU) and Microwave Sounding Unit (MSU) CDRs. Recent research has reduced but not eliminated discrepancies between SSU CDRs developed by NOAA and the UK Meteorological Office. The MSU CDRs from NOAA and Remote Sensing Systems are in closer agreement than the CDR from the University of Alabama in Huntsville. The latter has a previously unreported inhomogeneity in 2005, revealed by an abrupt increase in the magnitude and spatial variability of T anomaly differences between CDRs. Although time-varying biases remain in both SSU and MSU CDRs, multiple linear regression analyses reveal consistent solar, El Nino–Southern Oscillation (ENSO), quasi-biennial oscillation, aerosol, and piecewise-linear trend signals. Together, these predictors explain 80 to 90% of the variance in the near-global-average T CDRs. The most important predictor variables (in terms of percent explained variance in near-global-average T) for lower stratospheric T measured by MSU are aerosols, solar variability, and ENSO. Trends explain the largest percentage of variance in observations from all three SSU channels. In MSU and SSU CDRs, piecewise-linear trends, with a 1995 break point, indicate cooling during 1979–1994 but no trend during 1995–2013 for MSU and during 1995–2005 for SSU. These observational findings provide a basis for evaluating climate model simulations of stratospheric temperature during the past 35 years.

Recalibration and merging of SSU observations for stratospheric temperature trend studies

Long-term observations from the Stratospheric Sounding Unit (SSU) during 1979–2006 onboard NOAA historical polar orbiting satellites were recalibrated for climate change investigation. A two-point linear calibration equation, with cold space and an internal blackbody warm target as end-point references, was used to transfer SSU raw counts data into radiances. The warm target temperature was represented by measurements from the space side thermistor on the blackbody, and the cold space radiance was assumed to be zero. Space view corrections due to an electrical interference were applied. Intersatellite calibration was conducted simultaneously by applying calibration offsets determined from residual intersatellite biases. The recalibration reached an accuracy of 0.1–0.2 K for global means and thus is expected to improve the consistency in stratospheric temperature time series in climate reanalyses. The recalibrated SSU radiances were further adjusted to develop Version 2 of the NOAA stratospheric temperature time series. The effects being adjusted included those from changes in instrument cell pressure and atmospheric carbon dioxide concentration, viewing angle differences, and semidiurnal tides due to orbital drift. Intersatellite biases were carefully removed to ensure smooth transitions between satellite pairs. Differences from Version 1 included improved radiance calibration, improved adjusting schemes for diurnal drift and intersatellite biases, removal of time-varying cell pressure adjustment for NOAA-9 channel 1, and excluding NOAA-7 channel 2 in the time series. In addition to the final merged data set, intermediate synthetic time series corresponding to different adjustments were also created to quantify their impact on the final trend as well as its reliability and uncertainty. Excellent matching between satellite pairs, especially the 7 year overlaps between NOAA-11 and NOAA-14 during 1997–2004, in intermediate as well as the final time series provided strong evidence on the validity of adjustments and thus confidence on the resulting trends. The Version 2 global mean trends for 1979–2006 were −0.69 ± 0.18, −0.77 ± 0.15, and −0.85 ± 0.15 K/decade for SSU channels 1, 2, and 3, representing temperatures of middle stratosphere, upper stratosphere, and stratosphere-mesosphere, respectively. Among these, cooling of channel 2 was stronger and channel 3 weaker than those in UK Met Office (UKMO) data by about 1 K during the entire SSU period from 1979 to 2006. Finally, the average of the channel 1 and channel 3 anomalies in Version 2 was close to channel 2 anomalies to within 0.2 K for the entire 1979–2006 period with identical trends. This feature was found consistent with chemistry-climate model simulations.

Stratospheric Temperature Trends over 1979–2015 Derived from Combined SSU, MLS, and SABER Satellite Observations

AbstractTemperature trends in the middle and upper stratosphere are evaluated using measurements from the Stratospheric Sounding Unit (SSU), combined with data from the Aura Microwave Limb Sounder (MLS) and Sounding of the Atmosphere Using Broadband Emission Radiometry (SABER) instruments. Data from MLS and SABER are vertically integrated to approximate the SSU weighting functions and combined with SSU to provide a data record spanning 1979–2015. Vertical integrals are calculated using empirically derived Gaussian weighting functions, which provide improved agreement with high-latitude SSU measurements compared to previously derived weighting functions. These merged SSU data are used to evaluate decadal-scale trends, solar cycle variations, and volcanic effects from the lower to the upper stratosphere. Episodic warming is observed following the volcanic eruptions of El Chichon (1982) and Mt. Pinatubo (1991), focused in the tropics in the lower stratosphere and in high latitudes in the middle and upper str...