Greenhouse Gas Signal Research Articles

Detectable Anthropogenic Intensification of the Summer Compound Hot and Dry Events Over Global Land Areas

AbstractCompound hot and dry events (CHDEs) pose an increasing threat to global warming and have received growing attention in recent decades. We conducted a detection and attribution analysis (D&A) using the optimal fingerprint technique to analyze the changes in observed CHDEs at continental and sub‐continental scales from 1961 to 2014. The responses of CHDEs changes to anthropogenic (ANT), greenhouse gases (GHG), aerosols (AER), and natural (NAT) forcings were obtained from the Coupled Model Intercomparison Project phase 6 forcing experiment. In a two‐signal D&A analysis that considered ANT and NAT simultaneously, ANT signals were robustly detected and separated from NAT in all continental domains and more than 65% of subcontinental domains (29 out of 44 domains). The trend attributed to the ANT forcing is comparable to the observed trend over domains where the ANT signal is detectable. We also conducted a three‐signal D&A analysis that regressed observation onto GHG, AER, and NAT simultaneously. The GHG signals were detectable in all continents except Oceania, and it contributed dominantly to the observed trend over 22 out of 29 regions where ANT signals had emerged. Multivariate biases correction can largely reduce the models' biases in simulating the marginal distribution and the dependence structure, which can provide a more accurate future risk of CDHEs. We found that limiting global warming to 1.5℃ rather than 2.0℃ can significantly mitigate the impact of compound events in most areas of global land, particularly in South America, Africa, and Oceania.

Anthropogenic Influence on Temperature Change in China over the Period 1901–2018

Abstract Human influence on regional warming since 1901 has received little attention because of limited data during the early period. This study investigates the relative contribution of different external forcings to observed annual, summer, and winter warming in China over the period 1901–2018. First, four observational datasets were compared to validate data representativeness, particularly during the early twentieth century. Observed temperature changes were then compared with outputs from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) based on an optimal fingerprinting method. Generally, both generations of climate models were able to reliably reproduce long-term warming in China over the period 1901–2018; however, they slightly underestimate the amplitude of annual and winter temperature increases. The observed annual warming of 1.54°C from 1901 to 2018 was more rapid than the global mean and was mostly attributable to the anthropogenic forcing signal. The three-signal detection analyses, including greenhouse gas (GHG), anthropogenic aerosol (AA), and natural external (NAT) forcings, indicated the detectable and distinct influence of GHG and AA signals on annual, summer, and winter temperatures during 1901–2018. For annual mean temperature, the GHG and AA contributed to 2.06°C (from 1.58° to 2.54°C) and −0.45°C (from −0.17° to −0.73°C) of observed change, respectively. The GHG signal was detectable from individual CMIP6 models and thus was indicative of the robustness of this influence. While during 1951–2018, GHG and AA were simultaneously detected in the summer temperatures based on the CMIP6 models; here, the AA cooling effects offset approximately 25% of GHG-induced warming.

Detectable Human Influence on Changes in Precipitation Extremes Across China

AbstractThis study explored the human influence on the observed changes in precipitation extremes across China for the period of 1961–2014 by the latest simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Changes in observations and CMIP6 simulations under different external forcings for annual total wet‐day precipitation (PRCPTOT), the number of heavy precipitation days (R10 mm), very wet days (R95p), extremely wet days (R99p), the maximum 1‐day precipitation amount (RX1day), and the maximum 5‐day precipitation amount (RX5day) are compared using a regularized optimal fingerprinting method. The results show that both anthropogenic (ANT) and greenhouse gas (GHG) experiments generally exhibit increasing trends in precipitation extremes over China, agreeing with the overall observed increases. The ANT influence is robustly detected for R95p, R99p, RX1day, and RX5day over China, with clear separation from the natural forcing. Additionally, the GHG signal is also detectable for R95p, R99p, and RX1day but separated from the natural and other ANT forcings for only R99p and RX1day and from the natural and ANT aerosol forcings for only RX5day. Thus, the intensified precipitation extremes can be attributed to human influence dominated by the GHG effect. However, detections fail for PRCPTOT and R10 mm. The ANT aerosol forcing may partly offset the role of the GHG responses to observed increases despite being undetectable. Based on the detection and attribution results, the observation‐constrained projections indicate that precipitation extremes are projected to significantly increase under the SSP2–4.5 and SSP5–8.5 scenarios but with a smaller magnitude compared to the raw CMIP6 outputs.

Anthropogenic Greenhouse Gas and Aerosol Contributions to Extreme Temperature Changes during 1951–2015

AbstractThis study conducted a detection and attribution analysis of the observed global and regional changes in extreme temperatures during 1951–2015. HadEX3 observations were compared with multimodel simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6) using an optimal fingerprinting technique. Annual maximum daily maximum and minimum temperatures (TXx and TNx; warm extremes) and annual minimum daily maximum and minimum temperatures (TXn and TNn; cold extremes) over land were analyzed considering global, continental, and subcontinental scales. Response patterns (fingerprints) of extreme temperatures to anthropogenic (ANT), greenhouse gases (GHG), aerosols (AA), and natural (NAT) forcings were obtained from CMIP6 forced simulations. The internal variability ranges were estimated from preindustrial control simulations. A two-signal detection analysis where the observations are regressed onto ANT and NAT fingerprints simultaneously reveals that ANT signals are robustly detected in separation from NAT over global and all continental domains (North and South America, Europe, Asia, and Oceania) for most of the extreme indices. ANT signals are also detected over many subcontinental regions, particularly for warm extremes (more than 60% of 33 subregions). A three-signal detection analysis that considers GHG, AA, and NAT fingerprints simultaneously demonstrates that GHG signals are detected in isolation from other external forcings over global, continental, and several subcontinental domains especially for warm extremes, explaining most of the observed warming. Moreover, AA influences are detected for warm extremes over Europe and Asia, indicating significant offsetting cooling contributions. Overall, human influences are detected more frequently, compared to previous studies, particularly for cold extremes, due to the extended period and the improved spatial coverage of observations.

Human influence on frequency of temperature extremes

We investigate the influence of external forcings on the frequency of temperature extremes over land at the global and continental scales by comparing HadEX3 observations and simulations from the Coupled Model Intercomparison Programme Phase 6 (CMIP6) project. We consider four metrics including warm days and nights (TX90p and TN90p) and cold days and nights (TX10p and TN10p). The observational dataset during 1951–2018 shows continued increases in the warm days and nights and decreases in the cold days and nights in most land areas in the years after 2010. The area of the so-called ‘warming hole’ in North America is much reduced in 1951–2018 compared with that in 1951–2010. The comparison between observation and simulations based on an optimal fingerprinting method shows that the anthropogenic forcing, dominated by greenhouse gases, plays the most important role in the changes of the frequency indices. Changes in CMIP6 multi-model mean response to all forcing need to be scaled down to best match the observations, indicating that the multi-model ensemble mean may have overestimated the observed changes. Analyses that involve signals from anthropogenic and natural external forcings confirm that the anthropogenic signal can be detected over global land as a whole and for most continents in all temperature indices. Analyses that include signals from greenhouse gas (GHG), anthropogenic aerosol (AA) and natural external (NAT) forcings show that the GHG signal is detected in all indices over the globe and most continents while the AA signal can be detected mainly in the warm extremes but not the cold extremes over the globe and most continents. The effect of NAT is negligible in most land areas. GHG’s warming effect is offset partially by AA’s cooling effect. The combined effects from both explain most of the observed changes over the globe and continents.

Proactively planning and operating energy storage for decarbonization: Recommendations for policymakers

Energy storage is poised for explosive growth as a result of technological innovation, expanded state mandates and incentive programs, and new participation models developed under Federal Energy Regulatory Commission Order 841. If storage is done right – charged using clean power, and discharged when grid needs are high and dirtier power can be displaced – it can be a key part of a low-cost, clean grid. But a growing body of literature suggests that storage could actually increase greenhouse gas (GHG) if not managed and incentivized properly.Optimizing storage to drive decarbonization, particularly in states with climate goals and renewable portfolio standards (RPS), will require a coordinated effort from grid operators, policymakers, utilities, developers and other stakeholders. Some key actions that need to be taken include incorporating carbon pricing into state and Independent System Operator (ISO) actions, developing market products to reward storage for supporting renewable energy, tailoring instruments like rate design, incentive programs, regulation, and grid planning/procurement, and providing storage with a GHG signal for co-optimization.These recommendations leverage findings and lessons from existing studies and can be applied to ensure other innovations like electric vehicles, distributed energy resources and thermal storage also have a pathway to decarbonization.

Detectability of the impacts of ozone-depleting substances and greenhouse gases upon stratospheric ozone accounting for nonlinearities in historical forcings

Abstract. We perform a formal attribution study of upper- and lower-stratospheric ozone changes using observations together with simulations from the Whole Atmosphere Community Climate Model. Historical model simulations were used to estimate the zonal-mean response patterns (“fingerprints”) to combined forcing by ozone-depleting substances (ODSs) and well-mixed greenhouse gases (GHGs), as well as to the individual forcing by each factor. Trends in the similarity between the searched-for fingerprints and homogenized observations of stratospheric ozone were compared to trends in pattern similarity between the fingerprints and the internally and naturally generated variability inferred from long control runs. This yields estimated signal-to-noise (S∕N) ratios for each of the three fingerprints (ODS, GHG, and ODS + GHG). In both the upper stratosphere (defined in this paper as 1 to 10 hPa) and lower stratosphere (40 to 100 hPa), the spatial fingerprints of the ODS + GHG and ODS-only patterns were consistently detectable not only during the era of maximum ozone depletion but also throughout the observational record (1984–2016). We also develop a fingerprint attribution method to account for forcings whose time evolutions are markedly nonlinear over the observational record. When the nonlinearity of the time evolution of the ODS and ODS + GHG signals is accounted for, we find that the S∕N ratios obtained with the stratospheric ODS and ODS + GHG fingerprints are enhanced relative to standard linear trend analysis. Use of the nonlinear signal detection method also reduces the detection time – the estimate of the date at which ODS and GHG impacts on ozone can be formally identified. Furthermore, by explicitly considering nonlinear signal evolution, the complete observational record can be used in the S∕N analysis, without applying piecewise linear regression and introducing arbitrary break points. The GHG-driven fingerprint of ozone changes was not statistically identifiable in either the upper- or lower-stratospheric SWOOSH data, irrespective of the signal detection method used. In the WACCM simulations of future climate change, the GHG signal is statistically identifiable between 2020 and 2030. Our findings demonstrate the importance of continued stratospheric ozone monitoring to improve estimates of the contributions of ODS and GHG forcing to global changes in stratospheric ozone.

Profiling wind and greenhouse gases by infrared-laser occultation: results from end-to-end simulations in windy air

Abstract. The new mission concept of microwave and infrared-laser occultation between low-Earth-orbit satellites (LMIO) is designed to provide accurate and long-term stable profiles of atmospheric thermodynamic variables, greenhouse gases (GHGs), and line-of-sight (l.o.s.) wind speed with focus on the upper troposphere and lower stratosphere (UTLS). While the unique quality of GHG retrievals enabled by LMIO over the UTLS has been recently demonstrated based on end-to-end simulations, the promise of l.o.s. wind retrieval, and of joint GHG and wind retrieval, has not yet been analyzed in any realistic simulation setting. Here we use a newly developed l.o.s. wind retrieval algorithm, which we embedded in an end-to-end simulation framework that also includes the retrieval of thermodynamic variables and GHGs, and analyze the performance of both stand-alone wind retrieval and joint wind and GHG retrieval. The wind algorithm utilizes LMIO laser signals placed on the inflection points at the wings of the highly symmetric C18OO absorption line near 4767 cm−1 and exploits transmission differences from a wind-induced Doppler shift. Based on realistic example cases for a diversity of atmospheric conditions, ranging from tropical to high-latitude winter, we find that the retrieved l.o.s. wind profiles are of high quality over the lower stratosphere under all conditions, i.e., unbiased and accurate to within about 2 m s−1 over about 15 to 35 km. The wind accuracy degrades into the upper troposphere due to the decreasing signal-to-noise ratio of the wind-induced differential transmission signals. The GHG retrieval in windy air is not vulnerable to wind speed uncertainties up to about 10 m s−1 but is found to benefit in the case of higher speeds from the integrated wind retrieval that enables correction of wind-induced Doppler shift of GHG signals. Overall both the l.o.s. wind and GHG retrieval results are strongly encouraging towards further development and implementation of a LMIO mission.

Long-term changes/trends in surface temperature and precipitation during the satellite era (1979–2012)

During the post-1979 period in which the satellite-based precipitation measurements with global coverage are available, global mean surface temperature rapidly increased up to late 1990s, followed by a period of temperature hiatus after about 1998/1999. Comparing observed surface temperature trends against the simulated ones by the CMIP5 historical experiments especially in the zonal mean context suggests that although the anthropogenic greenhouse-gases (GHG) forcing has played a major role, in addition to the anthropogenic aerosols and various natural forcings, the effects from decadal-to-interdecadal-scale internal modes specifically the Pacific Decadal Oscillation (PDO) are also very strong. Evident temperature changes associated with the PDO’s phase shift are seen in the Pacific basin, with decadal-scale cooling in the tropical central-eastern Pacific and most of the east basin and concurrent warming in the subtropics of both hemispheres, even though the PDO’s net effect on global mean temperature is relatively weak. The Atlantic Multidecadal Oscillation (AMO) also changed its phase in the mid-1990s, and hence its possible impact is estimated and assessed as well. However, comparisons with CMIP5 simulations suggest that the AMO may have not contributed as significantly as the PDO in terms of the changes/trends in global surface temperature, even though the data analysis technique used here suggests otherwise. Long-term precipitation changes or trends during the post-1979 period are further shown to have been modulated by the two major factors: anthropogenic GHG and PDO, in addition to the relatively weak effects from aerosols and natural forcings. The spatial patterns of observed precipitation trends in the Pacific, including reductions in the tropical central-eastern Pacific and increases in the tropical western Pacific and along the South Pacific Convergence Zone, manifest the PDO’s contributions. Removing the PDO effect from the total precipitation trends makes the spatial structures of precipitation trends more similar to those simulated by CMIP5 historical full forcing experiments particularly in the context of zonal-mean results. This also confirms that in spite of the PDO effect specifically on regional scales, the anthropogenic GHG signals are still discernible in observed precipitation during the time period. Following the increase of GHG, precipitation tends to increase roughly along the climatological ITCZ and decrease south of the equator and in the subtropics of both hemispheres.

On North American Decadal Climate for 2011–20

Abstract The predictability of North American climate is diagnosed by taking into account both forced climate change and natural decadal-scale climate variability over the next decade. In particular, the “signal” in North American surface air temperature and precipitation over 2011–20 associated with the expected change in boundary conditions related to future anthropogenic greenhouse gas (GHG) forcing, as well as the “noise” around that signal due to internally generated ocean–atmosphere variability, is estimated. The structural uncertainty in the estimate of decadal predictability is diagnosed by examining the sensitivity to plausible scenarios for the GHG-induced change in boundary forcing, the model dependency of the forced signals, and the dependency on methods for estimating internal decadal noise. The signal-to-noise analysis by the authors is thus different from other published decadal prediction studies, in that this study does not follow a trajectory from a particular initial state but rather considers the statistics of internal variability in comparison with the GHG signal. The 2011–20 decadal signal is characterized by surface warming over the entire North American continent, precipitation decreases over the contiguous United States, and precipitation increases over Canada relative to 1971–2000 climatological conditions. The signs of these forced responses are robust across different sea surface temperature (SST) scenarios and the different models employed, though the amplitude of the response differs. The North American decadal noise is considerably smaller than the signal associated with boundary forcing, implying a potential for high forecast skill for 2011–20 North American climate even for prediction methods that do not attempt to initialize climate models. However, the results do suggest that initialized decadal predictions, which seek to forecast externally forced signals and also constrain the internal variability, could potentially improve upon uninitialized methods in regions where the external signal is small relative to internal variability.

Detection of the greenhouse gas signal from space: A progress report

The spatial distribution of the expected changes in the atmospheric water vapor content and cloud cover, as predicted by the transient greenhouse gas (GHG) simulation of Hansen et al. (1988), are examined to determine whether the signals would be large enough and unique enough to be useful in a GHG detection study. The nature of the predicted GHG signal was first examined using the transient CO2 run from the GISS ocean/atmosphere general circulation model. Next, the remotely sensed irradiance characteristics data (as the measure of water vapor content) supplied by the HIRS/MSU sensors for the area of the equatorial cold tongue region (the region in which there are no measurement stations). It is shown that HIRS/MSU signals can provide data necessary for detecting GHS signals in atmospheric moisture for regions where ground observations are not possible.