Roy Spencer - Profile on Academia.edu (original) (raw)
Papers by Roy Spencer
Remote Sensing, Sep 15, 2010
Updated tropical lower tropospheric temperature datasets covering the period 1979-2009 are presen... more Updated tropical lower tropospheric temperature datasets covering the period 1979-2009 are presented and assessed for accuracy based upon recent publications and several analyses conducted here. We conclude that the lower tropospheric temperature (T LT ) trend over these 31 years is +0.09 ± 0.03 °C decade -1 . Given that the surface temperature (T sfc ) trends from three different groups agree extremely closely among themselves (~ +0.12 °C decade -1 ) this indicates that the -scaling ratio‖ (SR, or ratio of atmospheric trend to surface trend: T LT /T sfc ) of the observations is ~0.8 ± 0.3. This is significantly different from the average SR calculated from the IPCC AR4 model simulations which is ~1.4. This result indicates the majority of AR4 simulations tend to portray significantly greater warming in the troposphere relative to the surface than is found in observations. The SR, as an internal, normalized metric of model behavior, largely avoids the confounding influence of short-term fluctuations such as El Niños which make direct comparison of trend magnitudes less confident, even over multi-decadal periods.
International Journal of Remote Sensing, Mar 8, 2018
The Intergovernmental Panel on Climate Change Assessment Report 5 (IPCC AR5, 2013) discussed bulk... more The Intergovernmental Panel on Climate Change Assessment Report 5 (IPCC AR5, 2013) discussed bulk atmospheric temperatures as indicators of climate variability and change. We examine four satellite datasets producing bulk tropospheric temperatures, based on microwave sounding units (MSUs), all updated since IPCC AR5. All datasets produce high correlations of anomalies versus independent observations from radiosondes (balloons), but differ somewhat in the metric of most interest, the linear trend beginning in 1979. The trend is an indicator of the response of the climate system to rising greenhouse gas concentrations and other forcings, and so is critical to understanding the climate. The satellite results indicate a range of nearglobal (+0.07 to +0.13°C decade -1 ) and tropical (+0.08 to +0.17°C decade -1 ) trends , and suggestions are presented to account for these differences. We show evidence that MSUs on National Oceanic and Atmospheric Administration's satellites (NOAA-12 and -14, 1990-2001+) contain spurious warming, especially noticeable in three of the four satellite datasets. Comparisons with radiosonde datasets independently adjusted for inhomogeneities and Reanalyses suggest the actual tropical (20°S-20°N ) trend is +0.10 ± 0.03°C decade -1 . This tropical result is over a factor of two less than the trend projected from the average of the IPCC climate model simulations for this same period (+0.27°C decade -1 ).
Remote Sensing, Jul 25, 2011
The sensitivity of the climate system to an imposed radiative imbalance remains the largest sourc... more The sensitivity of the climate system to an imposed radiative imbalance remains the largest source of uncertainty in projections of future anthropogenic climate change. Here we present further evidence that this uncertainty from an observational perspective is largely due to the masking of the radiative feedback signal by internal radiative forcing, probably due to natural cloud variations. That these internal radiative forcings exist and likely corrupt feedback diagnosis is demonstrated with lag regression analysis of satellite and coupled climate model data, interpreted with a simple forcing-feedback model. While the satellite-based metrics for the period 2000-2010 depart substantially in the direction of lower climate sensitivity from those similarly computed from coupled climate models, we find that, with traditional methods, it is not possible to accurately quantify this discrepancy in terms of the feedbacks which determine climate sensitivity. It is concluded that atmospheric feedback diagnosis of the climate system remains an unsolved problem, due primarily to the inability to distinguish between radiative forcing and radiative feedback in satellite radiative budget observations.
The sensitivity of the climate system to an imposed radiative imbalance remains the largest sourc... more The sensitivity of the climate system to an imposed radiative imbalance remains the largest source of uncertainty in projections of future anthropogenic climate change. Here we present further evidence that this uncertainty from an observational perspective is largely due to the masking of the radiative feedback signal by internal radiative forcing, probably due to natural cloud variations. That these internal radiative forcings exist and likely corrupt feedback diagnosis is demonstrated with lag regression analysis of satellite and coupled climate model data, interpreted with a simple forcingfeedback model. While the satellite-based metrics for the period 2000-2010 depart substantially in the direction of lower climate sensitivity from those similarly computed from coupled climate models, we find it is not possible with current methods to quantify this discrepancy in terms of the feedbacks which determine climate sensitivity. It is concluded that atmospheric feedback diagnosis of t...
Asia-Pacific Journal of Atmospheric Sciences, 2013
Global average ocean temperature variations to 2,000 m depth during 1955-2011 are simulated with ... more Global average ocean temperature variations to 2,000 m depth during 1955-2011 are simulated with a 40 layer 1D forcingfeedback-mixing model for three forcing cases. The first case uses standard anthropogenic and volcanic external radiative forcings. The second adds non-radiative internal forcing (ocean mixing changes initiated in the top 200 m) proportional to the Multivariate ENSO Index (MEI) to represent an internal mode of natural variability. The third case further adds ENSO-related radiative forcing proportional to MEI as a possible natural cloud forcing mechanism associated with atmospheric circulation changes. The model adjustable parameters are net radiative feedback, effective diffusivities, and internal radiative (e.g., cloud) and non-radiative (ocean mixing) forcing coefficients at adjustable time lags. Model output is compared to Levitus ocean temperature changes in 50 m layers during 1955-2011 to 700 m depth, and to lag regression coefficients between satellite radiative flux variations and sea surface temperature between 2000 and 2010. A net feedback parameter of 1.7 W m -2 K -1 with only anthropogenic and volcanic forcings increases to 2.8 W m -2 K -1 when all ENSO forcings (which are one-third radiative) are included, along with better agreement between model and observations. The results suggest ENSO can influence multi-decadal temperature trends, and that internal radiative forcing of the climate system affects the diagnosis of feedbacks. Also, the relatively small differences in model ocean warming associated with the three cases suggests that the observed levels of ocean warming since the 1950s is not a very strong constraint on our estimates of climate sensitivity.
Remote Sensing, 2011
The sensitivity of the climate system to an imposed radiative imbalance remains the largest sourc... more The sensitivity of the climate system to an imposed radiative imbalance remains the largest source of uncertainty in projections of future anthropogenic climate change. Here we present further evidence that this uncertainty from an observational perspective is largely due to the masking of the radiative feedback signal by internal radiative forcing, probably due to natural cloud variations. That these internal radiative forcings exist and likely corrupt feedback diagnosis is demonstrated with lag regression analysis of satellite and coupled climate model data, interpreted with a simple forcing-feedback model. While the satellite-based metrics for the period 2000-2010 depart substantially in the direction of lower climate sensitivity from those similarly computed from coupled climate models, we find that, with traditional methods, it is not possible to accurately quantify this discrepancy in terms of the feedbacks which determine climate sensitivity. It is concluded that atmospheric feedback diagnosis of the climate system remains an unsolved problem, due primarily to the inability to distinguish between radiative forcing and radiative feedback in satellite radiative budget observations.
Remote Sensing, 2010
Updated tropical lower tropospheric temperature datasets covering the period 1979-2009 are presen... more Updated tropical lower tropospheric temperature datasets covering the period 1979-2009 are presented and assessed for accuracy based upon recent publications and several analyses conducted here. We conclude that the lower tropospheric temperature (T LT ) trend over these 31 years is +0.09 ± 0.03 °C decade -1 . Given that the surface temperature (T sfc ) trends from three different groups agree extremely closely among themselves (~ +0.12 °C decade -1 ) this indicates that the -scaling ratio‖ (SR, or ratio of atmospheric trend to surface trend: T LT /T sfc ) of the observations is ~0.8 ± 0.3. This is significantly different from the average SR calculated from the IPCC AR4 model simulations which is ~1.4. This result indicates the majority of AR4 simulations tend to portray significantly greater warming in the troposphere relative to the surface than is found in observations. The SR, as an internal, normalized metric of model behavior, largely avoids the confounding influence of short-term fluctuations such as El Niños which make direct comparison of trend magnitudes less confident, even over multi-decadal periods.
Response : Microwave Sounding Units and Global Warming
Science, 1991
Journal of the Atmospheric Sciences, 1998
The second WetNet Precipitation Intercomparison Project (PIP-2) evaluates the performance of 20 s... more The second WetNet Precipitation Intercomparison Project (PIP-2) evaluates the performance of 20 satellite precipitation retrieval algorithms, implemented for application with Special Sensor Microwave/Imager (SSM/I) passive microwave (PMW) measurements and run for a set of rainfall case studies at full resolution-instantaneous space-timescales. The cases are drawn from over the globe during all seasons, for a period of 7 yr, over a 60ЊN-17ЊS latitude range. Ground-based data were used for the intercomparisons, principally based on radar measurements but also including rain gauge measurements. The goals of PIP-2 are 1) to improve performance and accuracy of different SSM/I algorithms at full resolution-instantaneous scales by seeking a better understanding of the relationship between microphysical signatures in the PMW measurements and physical laws employed in the algorithms; 2) to evaluate the pros and cons of individual algorithms and their subsystems in order to seek optimal ''front-end'' combined algorithms; and 3) to demonstrate that PMW algorithms generate acceptable instantaneous rain estimates. It is found that the bias uncertainty of many current PMW algorithms is on the order of Ϯ30%. This level is below that of the radar and rain gauge data specially collected for the study, so that it is not possible to objectively select a best algorithm based on the ground data validation approach. By decomposing the intercomparisons into effects due to rain detection (screening) and effects due to brightness temperature-rain rate conversion, differences among the algorithms are partitioned by rain area and rain intensity. For ocean, the screening differences mainly affect the light rain rates, which do not contribute significantly to area-averaged rain rates. The major sources of differences in mean rain rates between individual algorithms stem from differences in how intense rain rates are calculated and the maximum rain rate allowed by a given algorithm. The general method of solution is not necessarily the determining factor in creating systematic rain-rate differences among groups of algorithms, as we find that the severity of the screen is the dominant factor in producing systematic group differences among land algorithms, while the input channel selection is the dominant factor in producing systematic group differences among ocean algorithms. The significance of these issues are examined through what is called ''fan map'' analysis. The paper concludes with a discussion on the role of intercomparison projects in seeking improvements to algorithms, and a suggestion on why moving beyond the ''ground truth'' validation approach by use of a calibration-quality forward model would be a step forward in seeking objective evaluation of individual algorithm performance and optimal algorithm design.
Journal of Geophysical Research, 2010
The impact of time-varying radiative forcing on the diagnosis of radiative feedback from satellit... more The impact of time-varying radiative forcing on the diagnosis of radiative feedback from satellite observations of the Earth is explored. Phase space plots of variations in global average temperature versus radiative flux reveal linear striations and spiral patterns in both satellite measurements and in output from coupled climate models. A simple forcingfeedback model is used to demonstrate that the linear striations represent radiative feedback upon nonradiatively forced temperature variations, while the spiral patterns are the result of time-varying radiative forcing generated internal to the climate system. Only in the idealized special case of instantaneous and then constant radiative forcing, a situation that probably never occurs either naturally or anthropogenically, can feedback be observed in the presence of unknown radiative forcing. This is true whether the unknown radiative forcing is generated internal or external to the climate system. In the general case, a mixture of both unknown radiative and nonradiative forcings can be expected, and the challenge for feedback diagnosis is to extract the signal of feedback upon nonradiatively forced temperature change in the presence of the noise generated by unknown time-varying radiative forcing. These results underscore the need for more accurate methods of diagnosing feedback from satellite data and for quantitatively relating those feedbacks to long-term climate sensitivity.
Journal of Climate, 1998
The merging procedure utilized to generate homogeneous time series of three deep-layer atmospheri... more The merging procedure utilized to generate homogeneous time series of three deep-layer atmospheric temperature products from the nine microwave sounding units (MSUs) is described. A critically important aspect in the process is determining and removing the bias each instrument possesses relative to a common base (here being NOAA-6). Special attention is given to the lower-tropospheric layer and the calculation of the bias of the NOAA-9 MSU and its rather considerable impact on the trend of the overall time series. We show that the bias is best calculated by a direct comparison between NOAA-6 and NOAA-9, though there other possible methods available, and is determined to be +0.50°C. Spurious variations of individual MSUs due to orbital drift and/or cyclic variations tied to the annual cycle are also identified and eliminated. In general, intersatellite biases for the three instruments that form the backbone of the time series (MSUs on NOAA-6, -10 and -12) are known to within 0.01°C. ...
Journal of Climate, 2008
Feedbacks are widely considered to be the largest source of uncertainty in determining the sensit... more Feedbacks are widely considered to be the largest source of uncertainty in determining the sensitivity of the climate system to increasing anthropogenic greenhouse gas concentrations, yet the ability to diagnose them from observations has remained controversial. Here a simple model is used to demonstrate that any nonfeedback source of top-of-atmosphere radiative flux variations can cause temperature variability, which then results in a positive bias in diagnosed feedbacks. This effect is demonstrated with daily random flux variations, as might be caused by stochastic fluctuations in low cloud cover. The daily noise in radiative flux then causes interannual and decadal temperature variations in the model’s 50-m-deep swamp ocean. The amount of bias in the feedbacks diagnosed from time-averaged model output depends upon the size of the nonfeedback flux variability relative to the surface temperature variability, as well as the sign and magnitude of the specified (true) feedback. For mo...
Journal of Climate, 2004
There is no single reference dataset of long-term global upper-air temperature observations, alth... more There is no single reference dataset of long-term global upper-air temperature observations, although several groups have developed datasets from radiosonde and satellite observations for climate-monitoring purposes. The existence of multiple data products allows for exploration of the uncertainty in signals of climate variations and change. This paper examines eight upper-air temperature datasets and quantifies the magnitude and uncertainty of various climate signals, including stratospheric quasi-biennial oscillation (QBO) and tropospheric ENSO signals, stratospheric warming following three major volcanic eruptions, the abrupt tropospheric warming of 1976-77, and multidecadal temperature trends. Uncertainty estimates are based both on the spread of signal estimates from the different observational datasets and on the inherent statistical uncertainties of the signal in any individual dataset. The large spread among trend estimates suggests that using multiple datasets to characterize large-scale upperair temperature trends gives a more complete characterization of their uncertainty than reliance on a single dataset. For other climate signals, there is value in using more than one dataset, because signal strengths vary. However, the purely statistical uncertainty of the signal in individual datasets is large enough to effectively encompass the spread among datasets. This result supports the notion of an 11th climate-monitoring principle, augmenting the 10 principles that have now been generally accepted (although not generally implemented) by the climate community. This 11th principle calls for monitoring key climate variables with multiple, independent observing systems for measuring the variable, and multiple, independent groups analyzing the data.
Energy Law Journal, Jul 1, 2011
Litigation regarding "climate change" allegedly caused by emissions of "greenhouse gases"-primari... more Litigation regarding "climate change" allegedly caused by emissions of "greenhouse gases"-primarily CO 2-has been winding its way through the federal court system for more than half a decade. The Supreme Court has now issued two opinions in climate change cases. The first opinion, in Massachusetts v. EPA, upheld a challenge to EPA's decision not to regulate CO 2 emissions and has led the EPA to begin rulemaking on greenhouse gases. The second, Connecticut v. AEP, shut the courthouse doors on cases seeking to enjoin CO 2 emissions under federal common law nuisance claims but left the door open to state law claims and possibly damages claims. With the doors to the federal courthouses still open at least a crack, and a spate of recent state complaints, climate litigation seems to be a new fact of life. As the initial challenges to justiciability are overcome, the next line in the sand may be challenges to the admissibility of plaintiff's scientific evidence. This article focuses on the admissibility of scientific testimony on causation in common law nuisance damages cases under the Daubert standard, which is followed in all federal courts and about half of the states' courts. The authors have collaborated to blend an analysis of scientific theories and legal principals. They conclude that based on the current state of climate science and the principles of Daubert, climate change theories are not yet well enough established to hold CO 2 emitters liable for damages in a court of law. * Mr. Harlow practices utility law and litigation with the Washington, D.C.-based firm Lukas, Nace, Gutierrez & Sachs, LLP. Although his focus is primarily energy and telecommunications, he has litigated cases before courts and agencies involving nearly all types of utilities. He has been an avid lay student of climate change for years. He wishes to acknowledge the assistance of Seattle attorney, Adam Jussel. * Dr. Spencer is a Principal Research Scientist at the University of Alabama in Huntsville. He earned a Ph.D. in meteorology from the University of Wisconsin in 1981. While at NASA, as a Senior Scientist for Climate Studies, he jointly received NASA's Exceptional Scientific Achievement Medal for his global temperature monitoring work with satellites. Dr. Spencer is the U.S. Science Team leader for the Advanced Microwave Scanning Radiometer on NASA's Aqua satellite. He has provided congressional testimony several times on the subject of global warming.
Oceanic rain retrievals from satellite passive 37 GHz scattering measurements
A technique for estimating the effect of scattering on the average brightness temperature, T(B), ... more A technique for estimating the effect of scattering on the average brightness temperature, T(B), is examined. This scattering method is based on the relation observed over land between the SMMR T(B) and radar-derived rain rate. The scattering algorithm was evaluated and a comparison of radar and SMMR images reveals a correlation between radar-reflectivity-derived rates and the SMMR rain rates. The limitations of the scattering technique are discussed. Graphs and images displaying the application of the scattering algorithm are presented.
Remote Sensing, Sep 15, 2010
Updated tropical lower tropospheric temperature datasets covering the period 1979-2009 are presen... more Updated tropical lower tropospheric temperature datasets covering the period 1979-2009 are presented and assessed for accuracy based upon recent publications and several analyses conducted here. We conclude that the lower tropospheric temperature (T LT ) trend over these 31 years is +0.09 ± 0.03 °C decade -1 . Given that the surface temperature (T sfc ) trends from three different groups agree extremely closely among themselves (~ +0.12 °C decade -1 ) this indicates that the -scaling ratio‖ (SR, or ratio of atmospheric trend to surface trend: T LT /T sfc ) of the observations is ~0.8 ± 0.3. This is significantly different from the average SR calculated from the IPCC AR4 model simulations which is ~1.4. This result indicates the majority of AR4 simulations tend to portray significantly greater warming in the troposphere relative to the surface than is found in observations. The SR, as an internal, normalized metric of model behavior, largely avoids the confounding influence of short-term fluctuations such as El Niños which make direct comparison of trend magnitudes less confident, even over multi-decadal periods.
International Journal of Remote Sensing, Mar 8, 2018
The Intergovernmental Panel on Climate Change Assessment Report 5 (IPCC AR5, 2013) discussed bulk... more The Intergovernmental Panel on Climate Change Assessment Report 5 (IPCC AR5, 2013) discussed bulk atmospheric temperatures as indicators of climate variability and change. We examine four satellite datasets producing bulk tropospheric temperatures, based on microwave sounding units (MSUs), all updated since IPCC AR5. All datasets produce high correlations of anomalies versus independent observations from radiosondes (balloons), but differ somewhat in the metric of most interest, the linear trend beginning in 1979. The trend is an indicator of the response of the climate system to rising greenhouse gas concentrations and other forcings, and so is critical to understanding the climate. The satellite results indicate a range of nearglobal (+0.07 to +0.13°C decade -1 ) and tropical (+0.08 to +0.17°C decade -1 ) trends , and suggestions are presented to account for these differences. We show evidence that MSUs on National Oceanic and Atmospheric Administration's satellites (NOAA-12 and -14, 1990-2001+) contain spurious warming, especially noticeable in three of the four satellite datasets. Comparisons with radiosonde datasets independently adjusted for inhomogeneities and Reanalyses suggest the actual tropical (20°S-20°N ) trend is +0.10 ± 0.03°C decade -1 . This tropical result is over a factor of two less than the trend projected from the average of the IPCC climate model simulations for this same period (+0.27°C decade -1 ).
Remote Sensing, Jul 25, 2011
The sensitivity of the climate system to an imposed radiative imbalance remains the largest sourc... more The sensitivity of the climate system to an imposed radiative imbalance remains the largest source of uncertainty in projections of future anthropogenic climate change. Here we present further evidence that this uncertainty from an observational perspective is largely due to the masking of the radiative feedback signal by internal radiative forcing, probably due to natural cloud variations. That these internal radiative forcings exist and likely corrupt feedback diagnosis is demonstrated with lag regression analysis of satellite and coupled climate model data, interpreted with a simple forcing-feedback model. While the satellite-based metrics for the period 2000-2010 depart substantially in the direction of lower climate sensitivity from those similarly computed from coupled climate models, we find that, with traditional methods, it is not possible to accurately quantify this discrepancy in terms of the feedbacks which determine climate sensitivity. It is concluded that atmospheric feedback diagnosis of the climate system remains an unsolved problem, due primarily to the inability to distinguish between radiative forcing and radiative feedback in satellite radiative budget observations.
The sensitivity of the climate system to an imposed radiative imbalance remains the largest sourc... more The sensitivity of the climate system to an imposed radiative imbalance remains the largest source of uncertainty in projections of future anthropogenic climate change. Here we present further evidence that this uncertainty from an observational perspective is largely due to the masking of the radiative feedback signal by internal radiative forcing, probably due to natural cloud variations. That these internal radiative forcings exist and likely corrupt feedback diagnosis is demonstrated with lag regression analysis of satellite and coupled climate model data, interpreted with a simple forcingfeedback model. While the satellite-based metrics for the period 2000-2010 depart substantially in the direction of lower climate sensitivity from those similarly computed from coupled climate models, we find it is not possible with current methods to quantify this discrepancy in terms of the feedbacks which determine climate sensitivity. It is concluded that atmospheric feedback diagnosis of t...
Asia-Pacific Journal of Atmospheric Sciences, 2013
Global average ocean temperature variations to 2,000 m depth during 1955-2011 are simulated with ... more Global average ocean temperature variations to 2,000 m depth during 1955-2011 are simulated with a 40 layer 1D forcingfeedback-mixing model for three forcing cases. The first case uses standard anthropogenic and volcanic external radiative forcings. The second adds non-radiative internal forcing (ocean mixing changes initiated in the top 200 m) proportional to the Multivariate ENSO Index (MEI) to represent an internal mode of natural variability. The third case further adds ENSO-related radiative forcing proportional to MEI as a possible natural cloud forcing mechanism associated with atmospheric circulation changes. The model adjustable parameters are net radiative feedback, effective diffusivities, and internal radiative (e.g., cloud) and non-radiative (ocean mixing) forcing coefficients at adjustable time lags. Model output is compared to Levitus ocean temperature changes in 50 m layers during 1955-2011 to 700 m depth, and to lag regression coefficients between satellite radiative flux variations and sea surface temperature between 2000 and 2010. A net feedback parameter of 1.7 W m -2 K -1 with only anthropogenic and volcanic forcings increases to 2.8 W m -2 K -1 when all ENSO forcings (which are one-third radiative) are included, along with better agreement between model and observations. The results suggest ENSO can influence multi-decadal temperature trends, and that internal radiative forcing of the climate system affects the diagnosis of feedbacks. Also, the relatively small differences in model ocean warming associated with the three cases suggests that the observed levels of ocean warming since the 1950s is not a very strong constraint on our estimates of climate sensitivity.
Remote Sensing, 2011
The sensitivity of the climate system to an imposed radiative imbalance remains the largest sourc... more The sensitivity of the climate system to an imposed radiative imbalance remains the largest source of uncertainty in projections of future anthropogenic climate change. Here we present further evidence that this uncertainty from an observational perspective is largely due to the masking of the radiative feedback signal by internal radiative forcing, probably due to natural cloud variations. That these internal radiative forcings exist and likely corrupt feedback diagnosis is demonstrated with lag regression analysis of satellite and coupled climate model data, interpreted with a simple forcing-feedback model. While the satellite-based metrics for the period 2000-2010 depart substantially in the direction of lower climate sensitivity from those similarly computed from coupled climate models, we find that, with traditional methods, it is not possible to accurately quantify this discrepancy in terms of the feedbacks which determine climate sensitivity. It is concluded that atmospheric feedback diagnosis of the climate system remains an unsolved problem, due primarily to the inability to distinguish between radiative forcing and radiative feedback in satellite radiative budget observations.
Remote Sensing, 2010
Updated tropical lower tropospheric temperature datasets covering the period 1979-2009 are presen... more Updated tropical lower tropospheric temperature datasets covering the period 1979-2009 are presented and assessed for accuracy based upon recent publications and several analyses conducted here. We conclude that the lower tropospheric temperature (T LT ) trend over these 31 years is +0.09 ± 0.03 °C decade -1 . Given that the surface temperature (T sfc ) trends from three different groups agree extremely closely among themselves (~ +0.12 °C decade -1 ) this indicates that the -scaling ratio‖ (SR, or ratio of atmospheric trend to surface trend: T LT /T sfc ) of the observations is ~0.8 ± 0.3. This is significantly different from the average SR calculated from the IPCC AR4 model simulations which is ~1.4. This result indicates the majority of AR4 simulations tend to portray significantly greater warming in the troposphere relative to the surface than is found in observations. The SR, as an internal, normalized metric of model behavior, largely avoids the confounding influence of short-term fluctuations such as El Niños which make direct comparison of trend magnitudes less confident, even over multi-decadal periods.
Response : Microwave Sounding Units and Global Warming
Science, 1991
Journal of the Atmospheric Sciences, 1998
The second WetNet Precipitation Intercomparison Project (PIP-2) evaluates the performance of 20 s... more The second WetNet Precipitation Intercomparison Project (PIP-2) evaluates the performance of 20 satellite precipitation retrieval algorithms, implemented for application with Special Sensor Microwave/Imager (SSM/I) passive microwave (PMW) measurements and run for a set of rainfall case studies at full resolution-instantaneous space-timescales. The cases are drawn from over the globe during all seasons, for a period of 7 yr, over a 60ЊN-17ЊS latitude range. Ground-based data were used for the intercomparisons, principally based on radar measurements but also including rain gauge measurements. The goals of PIP-2 are 1) to improve performance and accuracy of different SSM/I algorithms at full resolution-instantaneous scales by seeking a better understanding of the relationship between microphysical signatures in the PMW measurements and physical laws employed in the algorithms; 2) to evaluate the pros and cons of individual algorithms and their subsystems in order to seek optimal ''front-end'' combined algorithms; and 3) to demonstrate that PMW algorithms generate acceptable instantaneous rain estimates. It is found that the bias uncertainty of many current PMW algorithms is on the order of Ϯ30%. This level is below that of the radar and rain gauge data specially collected for the study, so that it is not possible to objectively select a best algorithm based on the ground data validation approach. By decomposing the intercomparisons into effects due to rain detection (screening) and effects due to brightness temperature-rain rate conversion, differences among the algorithms are partitioned by rain area and rain intensity. For ocean, the screening differences mainly affect the light rain rates, which do not contribute significantly to area-averaged rain rates. The major sources of differences in mean rain rates between individual algorithms stem from differences in how intense rain rates are calculated and the maximum rain rate allowed by a given algorithm. The general method of solution is not necessarily the determining factor in creating systematic rain-rate differences among groups of algorithms, as we find that the severity of the screen is the dominant factor in producing systematic group differences among land algorithms, while the input channel selection is the dominant factor in producing systematic group differences among ocean algorithms. The significance of these issues are examined through what is called ''fan map'' analysis. The paper concludes with a discussion on the role of intercomparison projects in seeking improvements to algorithms, and a suggestion on why moving beyond the ''ground truth'' validation approach by use of a calibration-quality forward model would be a step forward in seeking objective evaluation of individual algorithm performance and optimal algorithm design.
Journal of Geophysical Research, 2010
The impact of time-varying radiative forcing on the diagnosis of radiative feedback from satellit... more The impact of time-varying radiative forcing on the diagnosis of radiative feedback from satellite observations of the Earth is explored. Phase space plots of variations in global average temperature versus radiative flux reveal linear striations and spiral patterns in both satellite measurements and in output from coupled climate models. A simple forcingfeedback model is used to demonstrate that the linear striations represent radiative feedback upon nonradiatively forced temperature variations, while the spiral patterns are the result of time-varying radiative forcing generated internal to the climate system. Only in the idealized special case of instantaneous and then constant radiative forcing, a situation that probably never occurs either naturally or anthropogenically, can feedback be observed in the presence of unknown radiative forcing. This is true whether the unknown radiative forcing is generated internal or external to the climate system. In the general case, a mixture of both unknown radiative and nonradiative forcings can be expected, and the challenge for feedback diagnosis is to extract the signal of feedback upon nonradiatively forced temperature change in the presence of the noise generated by unknown time-varying radiative forcing. These results underscore the need for more accurate methods of diagnosing feedback from satellite data and for quantitatively relating those feedbacks to long-term climate sensitivity.
Journal of Climate, 1998
The merging procedure utilized to generate homogeneous time series of three deep-layer atmospheri... more The merging procedure utilized to generate homogeneous time series of three deep-layer atmospheric temperature products from the nine microwave sounding units (MSUs) is described. A critically important aspect in the process is determining and removing the bias each instrument possesses relative to a common base (here being NOAA-6). Special attention is given to the lower-tropospheric layer and the calculation of the bias of the NOAA-9 MSU and its rather considerable impact on the trend of the overall time series. We show that the bias is best calculated by a direct comparison between NOAA-6 and NOAA-9, though there other possible methods available, and is determined to be +0.50°C. Spurious variations of individual MSUs due to orbital drift and/or cyclic variations tied to the annual cycle are also identified and eliminated. In general, intersatellite biases for the three instruments that form the backbone of the time series (MSUs on NOAA-6, -10 and -12) are known to within 0.01°C. ...
Journal of Climate, 2008
Feedbacks are widely considered to be the largest source of uncertainty in determining the sensit... more Feedbacks are widely considered to be the largest source of uncertainty in determining the sensitivity of the climate system to increasing anthropogenic greenhouse gas concentrations, yet the ability to diagnose them from observations has remained controversial. Here a simple model is used to demonstrate that any nonfeedback source of top-of-atmosphere radiative flux variations can cause temperature variability, which then results in a positive bias in diagnosed feedbacks. This effect is demonstrated with daily random flux variations, as might be caused by stochastic fluctuations in low cloud cover. The daily noise in radiative flux then causes interannual and decadal temperature variations in the model’s 50-m-deep swamp ocean. The amount of bias in the feedbacks diagnosed from time-averaged model output depends upon the size of the nonfeedback flux variability relative to the surface temperature variability, as well as the sign and magnitude of the specified (true) feedback. For mo...
Journal of Climate, 2004
There is no single reference dataset of long-term global upper-air temperature observations, alth... more There is no single reference dataset of long-term global upper-air temperature observations, although several groups have developed datasets from radiosonde and satellite observations for climate-monitoring purposes. The existence of multiple data products allows for exploration of the uncertainty in signals of climate variations and change. This paper examines eight upper-air temperature datasets and quantifies the magnitude and uncertainty of various climate signals, including stratospheric quasi-biennial oscillation (QBO) and tropospheric ENSO signals, stratospheric warming following three major volcanic eruptions, the abrupt tropospheric warming of 1976-77, and multidecadal temperature trends. Uncertainty estimates are based both on the spread of signal estimates from the different observational datasets and on the inherent statistical uncertainties of the signal in any individual dataset. The large spread among trend estimates suggests that using multiple datasets to characterize large-scale upperair temperature trends gives a more complete characterization of their uncertainty than reliance on a single dataset. For other climate signals, there is value in using more than one dataset, because signal strengths vary. However, the purely statistical uncertainty of the signal in individual datasets is large enough to effectively encompass the spread among datasets. This result supports the notion of an 11th climate-monitoring principle, augmenting the 10 principles that have now been generally accepted (although not generally implemented) by the climate community. This 11th principle calls for monitoring key climate variables with multiple, independent observing systems for measuring the variable, and multiple, independent groups analyzing the data.
Energy Law Journal, Jul 1, 2011
Litigation regarding "climate change" allegedly caused by emissions of "greenhouse gases"-primari... more Litigation regarding "climate change" allegedly caused by emissions of "greenhouse gases"-primarily CO 2-has been winding its way through the federal court system for more than half a decade. The Supreme Court has now issued two opinions in climate change cases. The first opinion, in Massachusetts v. EPA, upheld a challenge to EPA's decision not to regulate CO 2 emissions and has led the EPA to begin rulemaking on greenhouse gases. The second, Connecticut v. AEP, shut the courthouse doors on cases seeking to enjoin CO 2 emissions under federal common law nuisance claims but left the door open to state law claims and possibly damages claims. With the doors to the federal courthouses still open at least a crack, and a spate of recent state complaints, climate litigation seems to be a new fact of life. As the initial challenges to justiciability are overcome, the next line in the sand may be challenges to the admissibility of plaintiff's scientific evidence. This article focuses on the admissibility of scientific testimony on causation in common law nuisance damages cases under the Daubert standard, which is followed in all federal courts and about half of the states' courts. The authors have collaborated to blend an analysis of scientific theories and legal principals. They conclude that based on the current state of climate science and the principles of Daubert, climate change theories are not yet well enough established to hold CO 2 emitters liable for damages in a court of law. * Mr. Harlow practices utility law and litigation with the Washington, D.C.-based firm Lukas, Nace, Gutierrez & Sachs, LLP. Although his focus is primarily energy and telecommunications, he has litigated cases before courts and agencies involving nearly all types of utilities. He has been an avid lay student of climate change for years. He wishes to acknowledge the assistance of Seattle attorney, Adam Jussel. * Dr. Spencer is a Principal Research Scientist at the University of Alabama in Huntsville. He earned a Ph.D. in meteorology from the University of Wisconsin in 1981. While at NASA, as a Senior Scientist for Climate Studies, he jointly received NASA's Exceptional Scientific Achievement Medal for his global temperature monitoring work with satellites. Dr. Spencer is the U.S. Science Team leader for the Advanced Microwave Scanning Radiometer on NASA's Aqua satellite. He has provided congressional testimony several times on the subject of global warming.
Oceanic rain retrievals from satellite passive 37 GHz scattering measurements
A technique for estimating the effect of scattering on the average brightness temperature, T(B), ... more A technique for estimating the effect of scattering on the average brightness temperature, T(B), is examined. This scattering method is based on the relation observed over land between the SMMR T(B) and radar-derived rain rate. The scattering algorithm was evaluated and a comparison of radar and SMMR images reveals a correlation between radar-reflectivity-derived rates and the SMMR rain rates. The limitations of the scattering technique are discussed. Graphs and images displaying the application of the scattering algorithm are presented.