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- Author or Editor: Cheng-Zhi Zou x
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Stratospheric Temperature Climate Data Record from Merged SSU and AMSU-A ObservationsAbstract
Observations from the Stratospheric Sounding Unit (SSU) on board historical NOAA polar-orbiting satellites have played a vital role in investigations of long-term trends and variability in the middle- and upper-stratospheric temperatures during 1979–2006. The successor to SSU is the Advanced Microwave Sounding Unit-A (AMSU-A) starting from 1998 until the present. Unfortunately, the two observations came from different sets of atmospheric layers, and the SSU weighting functions varied with time and location, posing a challenge to merge them with sufficient accuracy for development of an extended SSU climate data record. This study proposes a variational approach for the merging problem, matching in both temperatures and weighting functions. The approach yields zero means with a small standard deviation and a negligible drift over time in the temperature differences between SSU and its extension to AMSU-A. These features made the approach appealing for reliable detection of long-term climate trends. The approach also matches weighting functions with high accuracy for SSU channels 1 and 2 and reasonable accuracy for channel 3. The total decreases in global mean temperatures found from the merged dataset were from 1.8 K in the middle stratosphere to 2.4 K in the upper stratosphere during 1979–2015. These temperature drops were associated with two segments of piecewise linear cooling trends, with those during the first period (1979–97) being much larger than those of the second period (1998–2015). These differences in temperature trends corresponded well to changes of the atmospheric ozone amount from depletion to recovery during the respective time periods, showing the influence of human decisions on climate change.
Abstract
Observations from the Stratospheric Sounding Unit (SSU) on board historical NOAA polar-orbiting satellites have played a vital role in investigations of long-term trends and variability in the middle- and upper-stratospheric temperatures during 1979–2006. The successor to SSU is the Advanced Microwave Sounding Unit-A (AMSU-A) starting from 1998 until the present. Unfortunately, the two observations came from different sets of atmospheric layers, and the SSU weighting functions varied with time and location, posing a challenge to merge them with sufficient accuracy for development of an extended SSU climate data record. This study proposes a variational approach for the merging problem, matching in both temperatures and weighting functions. The approach yields zero means with a small standard deviation and a negligible drift over time in the temperature differences between SSU and its extension to AMSU-A. These features made the approach appealing for reliable detection of long-term climate trends. The approach also matches weighting functions with high accuracy for SSU channels 1 and 2 and reasonable accuracy for channel 3. The total decreases in global mean temperatures found from the merged dataset were from 1.8 K in the middle stratosphere to 2.4 K in the upper stratosphere during 1979–2015. These temperature drops were associated with two segments of piecewise linear cooling trends, with those during the first period (1979–97) being much larger than those of the second period (1998–2015). These differences in temperature trends corresponded well to changes of the atmospheric ozone amount from depletion to recovery during the respective time periods, showing the influence of human decisions on climate change.
Abstract
Warm target effect and diurnal drift errors are the main sources of uncertainties in the trend determination from the NOAA Microwave Sounding Unit (MSU) observations. Currently, there are two methods to correct the warm target effect: 1) finding a best root-level (level-1c) calibration nonlinearity using simultaneous nadir overpass (SNO) matchups to minimize this effect for each scene radiance, and 2) finding a best-fit empirical relationship between the correction term of the end-level gridded brightness temperature and warm target temperature and then removing the best fit from the unadjusted time series. The former corrects the warm target effect before the diurnal drift adjustment and provides more accurate, warm target effect–minimized, level-1c scene radiances for reanalysis applications. The latter corrects the warm target effect at the end-level merging step, which depend on the diurnal drift correction that occurred at a previous step. Although minimized, the first method still leaves small residual warm target–related errors due to imperfect calibrations. This study demonstrates that when the diurnal drift effect is negligible, a combination of the two methods completely removes warm target effect and produces an invariant trend that is independent of the level-1c calibration in the SNO framework. The conclusion is directly applicable to the MSU channel-2 oceanic midtropospheric temperature (T 2) and global channel-3 upper-tropospheric temperature (T 3) and channel-4 lower-stratospheric temperature (T 4), which satisfy the condition of negligible diurnal drift effect. On the basis of these results, version 1.2 of the National Environmental Satellite, Data, and Information Service (NESDIS)–Center for Satellite Applications and Research (STAR) multisatellite MSU time series was constructed, including all T 2, T 3, and T 4 products. In addition, a diurnal drift correction based on the Remote Sensing Systems diurnal anomalies was applied to the T 2 product, which produces consistent climate trends between land and ocean. The global long-term climate trends for T 2 and T 4 derived from the STAR V1.2 dataset are, respectively, 0.18 ± 0.05 and −0.39 ± 0.36 K decade−1 during 1979–2006; the T 3 trend is 0.11 ± 0.08 K decade−1 for 1981–2006.
Abstract
Warm target effect and diurnal drift errors are the main sources of uncertainties in the trend determination from the NOAA Microwave Sounding Unit (MSU) observations. Currently, there are two methods to correct the warm target effect: 1) finding a best root-level (level-1c) calibration nonlinearity using simultaneous nadir overpass (SNO) matchups to minimize this effect for each scene radiance, and 2) finding a best-fit empirical relationship between the correction term of the end-level gridded brightness temperature and warm target temperature and then removing the best fit from the unadjusted time series. The former corrects the warm target effect before the diurnal drift adjustment and provides more accurate, warm target effect–minimized, level-1c scene radiances for reanalysis applications. The latter corrects the warm target effect at the end-level merging step, which depend on the diurnal drift correction that occurred at a previous step. Although minimized, the first method still leaves small residual warm target–related errors due to imperfect calibrations. This study demonstrates that when the diurnal drift effect is negligible, a combination of the two methods completely removes warm target effect and produces an invariant trend that is independent of the level-1c calibration in the SNO framework. The conclusion is directly applicable to the MSU channel-2 oceanic midtropospheric temperature (T 2) and global channel-3 upper-tropospheric temperature (T 3) and channel-4 lower-stratospheric temperature (T 4), which satisfy the condition of negligible diurnal drift effect. On the basis of these results, version 1.2 of the National Environmental Satellite, Data, and Information Service (NESDIS)–Center for Satellite Applications and Research (STAR) multisatellite MSU time series was constructed, including all T 2, T 3, and T 4 products. In addition, a diurnal drift correction based on the Remote Sensing Systems diurnal anomalies was applied to the T 2 product, which produces consistent climate trends between land and ocean. The global long-term climate trends for T 2 and T 4 derived from the STAR V1.2 dataset are, respectively, 0.18 ± 0.05 and −0.39 ± 0.36 K decade−1 during 1979–2006; the T 3 trend is 0.11 ± 0.08 K decade−1 for 1981–2006.
Abstract
The Advanced Microwave Sounding Unit-A (AMSU-A, 1998–present) not only continues but surpasses the Microwave Sounding Unit’s (MSU, 1978–2006) capability in atmospheric temperature observation. It provides valuable satellite measurements for higher vertical resolution and long-term climate change research and trend monitoring. This study presented methodologies for generating 11 channels of AMSU-A-only atmospheric temperature data records from the lower troposphere to the top of the stratosphere. The recalibrated AMSU-A level 1c radiances recently developed by the Center for Satellite Applications and Research group were used. The recalibrated radiances were adjusted to a consistent sensor incidence angle (nadir), channel frequencies (prelaunch-specified central frequencies), and observation time (local solar noon time). Radiative transfer simulations were used to correct the sensor incidence angle effect and the National Oceanic and Atmospheric Administration-15 (NOAA-15) channel 6 frequency shift. Multiyear averaged diurnal/semidiurnal anomaly climatologies from climate reanalysis as well as climate model simulations were used to adjust satellite observations to local solar noon time. Adjusted AMSU-A measurements from six satellites were carefully quality controlled and merged to generate 13+ years (1998–2011) of a monthly 2.5° × 2.5° gridded atmospheric temperature data record. Major trend features in the AMSU-A-only atmospheric temperature time series, including global mean temperature trends and spatial trend patterns, were summarized.
Abstract
The Advanced Microwave Sounding Unit-A (AMSU-A, 1998–present) not only continues but surpasses the Microwave Sounding Unit’s (MSU, 1978–2006) capability in atmospheric temperature observation. It provides valuable satellite measurements for higher vertical resolution and long-term climate change research and trend monitoring. This study presented methodologies for generating 11 channels of AMSU-A-only atmospheric temperature data records from the lower troposphere to the top of the stratosphere. The recalibrated AMSU-A level 1c radiances recently developed by the Center for Satellite Applications and Research group were used. The recalibrated radiances were adjusted to a consistent sensor incidence angle (nadir), channel frequencies (prelaunch-specified central frequencies), and observation time (local solar noon time). Radiative transfer simulations were used to correct the sensor incidence angle effect and the National Oceanic and Atmospheric Administration-15 (NOAA-15) channel 6 frequency shift. Multiyear averaged diurnal/semidiurnal anomaly climatologies from climate reanalysis as well as climate model simulations were used to adjust satellite observations to local solar noon time. Adjusted AMSU-A measurements from six satellites were carefully quality controlled and merged to generate 13+ years (1998–2011) of a monthly 2.5° × 2.5° gridded atmospheric temperature data record. Major trend features in the AMSU-A-only atmospheric temperature time series, including global mean temperature trends and spatial trend patterns, were summarized.
Abstract
The National Ice Center relies upon a coupled ice–ocean model called the Polar Ice Prediction System (PIPS) to provide guidance for its 24–120-h sea ice forecasts. Here forecast skill assessments of the sea ice concentration (C) fields from PIPS for the period 1 May 2000–31 May 2002 are presented. Methods of measuring the sea ice forecast skill are adapted from the meteorological literature and applied to locations where the forecast or analysis sea ice fields changed by at least ±5%. The forecast skill referenced to climatology was high (>0.85, relative to a maximum score of 1.0) for all months examined. This is because interannual variability in the climatology, which is used as a reference field, is much greater than the day-to-day variability in the forecast field. The PIPS forecasts were also evaluated against persistence and combined climatological–persistence forecasts. Compared to persistence, the 24-h forecast was found to be skillful (>0.2) for all months studied except during the freeze-up months of December 2000 and January 2001. Relative to the combined reference field, the 24-h forecast was also positive for the non-freeze-up months; however, the skill scores were lower (∼0.1). During the poorly performing freeze-up months, a linear combination of persistence (∼95% weight) and climatology (∼5% weight) appears to provide the best available sea ice forecast.
To examine the less restrictive question of whether PIPS can forecast sea ice concentration changes, independent of the magnitude of the changes, “threat indexes” patterned after methods developed for tornado forecasting were established. Two specific questions were addressed with this technique. The first question is: What is the skill of forecasting locations at which a decrease in sea ice concentration has occurred? The second question is: Does PIPS correctly forecast melt-out regions? Using the more relaxed criterion of a threat index for defining correct forecasts, it was found that PIPS correctly made 24-h forecasts of decreasing sea ice concentration ∼10%–15% of the time (it also correctly forecast increasing sea ice concentration an additional ∼10%–15% of the time). However, PIPS correctly forecast melt-out conditions <5% of the time, suggesting that there may be deficiencies in the PIPS parameterization of marginal ice zone processes and/or uncertainties in the atmospheric–oceanic fields that force PIPS.
Abstract
The National Ice Center relies upon a coupled ice–ocean model called the Polar Ice Prediction System (PIPS) to provide guidance for its 24–120-h sea ice forecasts. Here forecast skill assessments of the sea ice concentration (C) fields from PIPS for the period 1 May 2000–31 May 2002 are presented. Methods of measuring the sea ice forecast skill are adapted from the meteorological literature and applied to locations where the forecast or analysis sea ice fields changed by at least ±5%. The forecast skill referenced to climatology was high (>0.85, relative to a maximum score of 1.0) for all months examined. This is because interannual variability in the climatology, which is used as a reference field, is much greater than the day-to-day variability in the forecast field. The PIPS forecasts were also evaluated against persistence and combined climatological–persistence forecasts. Compared to persistence, the 24-h forecast was found to be skillful (>0.2) for all months studied except during the freeze-up months of December 2000 and January 2001. Relative to the combined reference field, the 24-h forecast was also positive for the non-freeze-up months; however, the skill scores were lower (∼0.1). During the poorly performing freeze-up months, a linear combination of persistence (∼95% weight) and climatology (∼5% weight) appears to provide the best available sea ice forecast.
To examine the less restrictive question of whether PIPS can forecast sea ice concentration changes, independent of the magnitude of the changes, “threat indexes” patterned after methods developed for tornado forecasting were established. Two specific questions were addressed with this technique. The first question is: What is the skill of forecasting locations at which a decrease in sea ice concentration has occurred? The second question is: Does PIPS correctly forecast melt-out regions? Using the more relaxed criterion of a threat index for defining correct forecasts, it was found that PIPS correctly made 24-h forecasts of decreasing sea ice concentration ∼10%–15% of the time (it also correctly forecast increasing sea ice concentration an additional ∼10%–15% of the time). However, PIPS correctly forecast melt-out conditions <5% of the time, suggesting that there may be deficiencies in the PIPS parameterization of marginal ice zone processes and/or uncertainties in the atmospheric–oceanic fields that force PIPS.
