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The Tropical Rainfall Measuring Mission (TRMM) satellite began operating in December 1997 and was shut down on 8 April 2015. Over the oceans, the microwave (MW) sensor aboard TRMM measures sea surface temperature, wind speed, and rain rate as well as atmospheric columnar water vapor and cloud liquid water. Improved calibration methods are applied to the TRMM Microwave Imager (TMI), and a 17-yr climate record of these environmental parameters is produced so as to be consistent with the climate records from 13 other MW sensors. These TMI retrievals are validated relative to in situ observations over its 17-yr mission life. All indications point to TMI being an extremely stable sensor capable of providing satellite climate records of unprecedented length and accuracy.

Space-based observations of ocean surface winds have been available for more than 25 years. To combine the observations from multiple sensors into one record with the accuracy required for climate studies requires a consistent methodology and calibration standard for the various instruments. This study describes a new geophysical model function (GMF) specifically developed for preparing the QuikSCAT winds to serve as a backbone of an ocean vector wind climate data record. This paper describes the methodology used and presents the quality of the reprocessed winds. The new Ku-2011 model function was developed using WindSat winds as a calibration truth. An extensive validation of the Ku-2011 winds was performed that focused on 1) proving the consistency of satellite winds from different sensors at all wind speed regimes; 2) exploring and understanding possible sources of bias in the QuikSCAT retrievals; 3) validating QuikSCAT wind speeds versus in situ observations, and comparing observed wind directions versus those from numerical models; 4) comparing satellite observations of high wind speeds with measurements obtained from aircraft flying into storms; 5) analyzing case studies of satellite-based observations of winds in tropical storms; and 6) illustrating how rain impacts QuikSCAT wind speed retrievals. The results show that the reprocessed QuikSCAT data are greatly improved in both speed and direction at high winds. Finally, there is a discussion on how these QuikSCAT results fit into a long-term effort toward creating a climate data record of ocean vector winds.

The Global Precipitation Measurement (GPM) Core Observatory was launched on 27 February 2014. One of the principal instruments on the spacecraft is the GPM Microwave Imager (GMI). This paper describes the absolute calibration of the GMI antenna temperature ( T A ) and the earth brightness temperature ( T B ). The deep-space observations taken on 20 May 2014, supplemented by nadir-viewing data, are used for the T A calibration. Data from two backlobe maneuvers are used to determine the primary reflector’s cold-space spillover, which is required to convert the T A into T B . The calibrated GMI observations are compared to predictions from an ocean radiative transfer model (RTM) using collocated WindSat ocean retrievals as input. The mean difference when averaged globally over 13 months does not exceed 0.1 K for any of the nine channels from 11 to 89 GHz. The RTM comparisons also show that there are no significant solar intrusion errors in the GMI hot load. The absolute accuracy of the GMI instrument is defined as the average ocean-viewing error of the measured T A or T B relative to the true T A or T B . Based on the analyses herein, the GMI absolute accuracy for T A is estimated to be about 0.1 K rms over all channels and 0.25 K rms over all channels for T B .

Climate models and satellite observations both indicate that the total amount of water in the atmosphere will increase at a rate of 7% per kelvin of surface warming. However, the climate models predict that global precipitation will increase at a much slower rate of 1 to 3% per kelvin. A recent analysis of satellite observations does not support this prediction of a muted response of precipitation to global warming. Rather, the observations suggest that precipitation and total atmospheric water have increased at about the same rate over the past two decades.

Temperature sounding microwave radiometers flown on polar-orbiting weather satellites provide a long-term, global-scale record of upper-atmosphere temperatures, beginning in late 1978 and continuing to the present. The focus of this paper is a lower-tropospheric temperature product constructed using measurements made by the Microwave Sounding Unit channel 2 and the Advanced Microwave Sounding Unit channel 5. The temperature weighting functions for these channels peak in the middle to upper troposphere. By using a weighted average of measurements made at different Earth incidence angles, the effective weighting function can be lowered so that it peaks in the lower troposphere. Previous versions of this dataset used general circulation model output to remove the effects of drifting local measurement time on the measured temperatures. This paper presents a method to optimize these adjustments using information from the satellite measurements themselves. The new method finds a global-mean land diurnal cycle that peaks later in the afternoon, leading to improved agreement between measurements made by co-orbiting satellites. The changes result in global-scale warming [global trend (70°S–80°N, 1979–2016) 5 0.174°C decade−1], ∼30% larger than our previous version of the dataset [global trend (70°S–80°N, 1979–2016) = 0.134°C decade−1]. This change is primarily due to the changes in the adjustment for drifting local measurement time. The new dataset shows more warming than most similar datasets constructed from satellites or radiosonde data. However, comparisons with total column water vapor over the oceans suggest that the new dataset may not show enough warming in the tropics.

Temperature sounding microwave radiometers flown on polar-orbiting weather satellites provide a long-term, global-scale record of upper-atmosphere temperatures, beginning in late 1978 and continuing to the present. The focus of this paper is the midtropospheric measurements made by the Microwave Sounding Unit (MSU) channel 2 and the Advanced Microwave Sounding Unit (AMSU) channel 5. Previous versions of the Remote Sensing Systems (RSS) dataset have used a diurnal climatology derived from general circulation model output to remove the effects of drifting local measurement time. This paper presents evidence that this previous method is not sufficiently accurate and presents several alternative methods to optimize these adjustments using information from the satellite measurements themselves. These are used to construct a number of candidate climate data records using measurements from 15 MSU and AMSU satellites. The new methods result in improved agreement between measurements made by different satellites at the same time. A method is chosen based on an optimized second harmonic adjustment to produce a new version of the RSS dataset, version 4.0. The new dataset shows substantially increased global-scale warming relative to the previous version of the dataset, particularly after 1998. The new dataset shows more warming than most other midtropospheric data records constructed from the same set of satellites. It is also shown that the new dataset is consistent with long-term changes in total column water vapor over the tropical oceans, lending support to its long-term accuracy.

Anthropogenic climate change has become clearly observable through many metrics. These include an increase in global annual temperatures, growing heat content of the oceans, and sea level rise owing to the melting of the polar ice sheets and glaciers. Now, Santer et al. report that a human-caused signal in the seasonal cycle of tropospheric temperature can also be measured (see the Perspective by Randel). They use satellite data and the anthropogenic “fingerprint” predicted by climate models to show the extent of the effects and discuss how these changes have been caused. Science , this issue p. eaas8806 ; see also p. 227 Human activity is causing changes in the seasonal cycle of tropospheric temperature. We provide scientific evidence that a human-caused signal in the seasonal cycle of tropospheric temperature has emerged from the background noise of natural variability. Satellite data and the anthropogenic “fingerprint” predicted by climate models show common large-scale changes in geographical patterns of seasonal cycle amplitude. These common features include increases in amplitude at mid-latitudes in both hemispheres, amplitude decreases at high latitudes in the Southern Hemisphere, and small changes in the tropics. Simple physical mechanisms explain these features. The model fingerprint of seasonal cycle changes is identifiable with high statistical confidence in five out of six satellite temperature datasets. Our results suggest that attribution studies with the changing seasonal cycle provide powerful evidence for a significant human effect on Earth’s climate.

Global mean surface and tropospheric temperatures have shown slower warming since 1998 than found in climate model simulations. A detailed analysis of observations and climate model simulations suggests that the observed influence of volcanic eruptions on tropospheric temperature has been significant, and that the discrepancy between climate simulations and observations is reduced by up to 15% when twenty-first century volcanic eruptions are accounted for in the models. Despite continued growth in atmospheric levels of greenhouse gases, global mean surface and tropospheric temperatures have shown slower warming since 1998 than previously 1 , 2 , 3 , 4 , 5 . Possible explanations for the slow-down include internal climate variability 3 , 4 , 6 , 7 , external cooling influences 1 , 2 , 4 , 8 , 9 , 10 , 11 and observational errors 12 , 13 . Several recent modelling studies have examined the contribution of early twenty-first-century volcanic eruptions 1 , 2 , 4 , 8 to the muted surface warming. Here we present a detailed analysis of the impact of recent volcanic forcing on tropospheric temperature, based on observations as well as climate model simulations. We identify statistically significant correlations between observations of stratospheric aerosol optical depth and satellite-based estimates of both tropospheric temperature and short-wave fluxes at the top of the atmosphere. We show that climate model simulations without the effects of early twenty-first-century volcanic eruptions overestimate the tropospheric warming observed since 1998. In two simulations with more realistic volcanic influences following the 1991 Pinatubo eruption, differences between simulated and observed tropospheric temperature trends over the period 1998 to 2012 are up to 15% smaller, with large uncertainties in the magnitude of the effect. To reduce these uncertainties, better observations of eruption-specific properties of volcanic aerosols are needed, as well as improved representation of these eruption-specific properties in climate model simulations.

This work describes a new climatology of cloud liquid water path (LWP), termed the University of Wisconsin (UWisc) climatology, derived from 18 yr of satellite-based passive microwave observations over the global oceans. The climatology is based on a modern retrieval methodology applied consistently to the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer (AMSR) for Earth Observing System (EOS) (AMSR-E) microwave sensors on eight different satellite platforms, beginning in 1988 and continuing through 2005. It goes beyond previously published climatologies by explicitly solving for the diurnal cycle of cloud liquid water by providing statistical error estimates, and includes a detailed discussion of possible systematic errors. A novel methodology for constructing the climatology is used in which a mean monthly diurnal cycle as well as monthly means of the liquid water path are derived simultaneously from the data on a 1° grid; the methodology also produces statistical errors for these quantities, which decrease toward the end of the time record as the number of observations increases. The derived diurnal cycles are consistent with previous findings in the tropics, but are also derived for higher latitudes and contain more information than in previous studies. The new climatology exhibits differences with previous observationally based climatologies and is found to be more consistent with the 40-yr ECMWF Re-Analysis (ERA-40) than are the previous climatologies. Potential systematic errors of the order of 15%–30% or higher exist in the LWP climatology. A previously unexplored source of systematic error is caused by the assumption that all microwave-based retrievals of LWP must make regarding the partitioning of cloud water and rainwater, which cannot be determined using microwave observations alone. The potentially large systematic errors that result may hamper the usefulness of microwave-based climatologies of both cloud liquid water and especially rain rate, particularly in certain regions of the tropics and midlatitudes where the separation of rain from liquid cloud water is most critical.

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.