519 publications from this institution
Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsKevin E. TrenberthKevin E. Trenberth heads the Climate Analysis Section of the National Center for Atmospheric Research (sponsored by the National Science Foundation) in Boulder, Colorado. He participated as a lead author of a chapter, as well as a lead author of both the Technical Summary and the Summary for Policy Makers of the third IPCC Working Group I report. He can be reached at (303) 497-1318 or trenbert@ucar.edu.
The Southern Oscillation is an important mode of operation of the atmosphere. Several indices of the Southern Oscillation have been recognized, but for many purposes an index that gives a continuous, homogeneous time series is required. Phase differences between various areas complicate the definition of a simple index but weighted values of seasonal surface pressure at Darwin and Tahiti seem suitable for reference purposes. Time series of short period correlation coefficients prove useful for revealing temporal variations in the character and amplitude of the Southern Oscillation and reveal times when it is not very prominent. Lag relationships are determined and used to indicate some aspects of the circulation near times of phase reversal of the oscillation. Spectral analysis showed most variance to be in the range of 2 to 10 year periodicities, and cross spectral calculations further indicated that only for the 2–10 year range, and mainly for the 3–6 year range, of periodicities is there large‐scale coherence and out‐of‐phase relationships between stations in the South Pacific and Darwin. This defines the time scale of the Southern Oscillation. The Southern Oscillation is not confined to tropical and subtropical latitudes but is clearly linked to mid‐latitude systems and long waves in the westerlies. East‐west circulations coupling the South Pacific Convergence Zone and the South Pacific high also appear to play an important role in the Southern Oscillation.
Factors involved in the recent pause in the rise of global mean temperatures are examined seasonally. For 1999 to 2012, the hiatus in surface warming is mainly evident in the central and eastern Pacific. It is manifested as strong anomalous easterly trade winds, distinctive sea-level pressure patterns, and large rainfall anomalies in the Pacific, which resemble the Pacific Decadal Oscillation (PDO). These features are accompanied by upper tropospheric teleconnection wave patterns that extend throughout the Pacific, to polar regions, and into the Atlantic. The extratropical features are particularly strong during winter. By using an idealized heating to force a comprehensive atmospheric model, the large negative anomalous latent heating associated with the observed deficit in central tropical Pacific rainfall is shown to be mainly responsible for the global quasi-stationary waves in the upper troposphere. The wave patterns in turn created persistent regional climate anomalies, increasing the odds of cold winters in Europe. Hence, tropical Pacific forcing of the atmosphere such as that associated with a negative phase of the PDO produces many of the pronounced atmospheric circulation anomalies observed globally during the hiatus. The slowdown in global warming has been identified predominately through changes in the Pacific Ocean. This study investigates the teleconnections and seasonal changes associated with the slowdown. The present forcing from the tropical Pacific is found to produce many of the changes in atmospheric circulation, for example, changes in the upper troposphere wave patterns increase the chances of cold European winters.
No abstract is provided for this article.
No abstract is provided for this article.
An assessment is made of how well the monthly mean surface temperatures for the decade of the 1980s are known. The sources of noise in the data, the numbers of observations, and the spatial coverage are appraised for comparison with the climate signal, and different analyzed results are compared to see how reproducible they are. The data are further evaluated by comparing anomalies of near-global monthly mean surface temperatures with those of global satellite channel 2 microwave sounding unit (MSU) temperatures for 144 months from 1979 to 1990. Very distinctive patterns are seen in the correlation coefficients, which range from high (>0.8) over the extratropical continents of the Northern Hemisphere, to moderate (∼0,5) over tropical and subtropical land areas, to very low over the southern oceans and tropical western Pacific. The physical difference between the two temperature measurements is one factor in these patterns. The correlation coefficient is a measure of the signal-to-noise ratio, and largest values are found where the climate signal is largest, but the spatial variation in the inherent noise in the surface observations over the oceans is the other major factor in accounting for the pattern. Over the oceans, sea surface temperatures (SSTS) are used in the surface dataset in place of surface air temperature and the Comprehensive Ocean-Atmosphere Data Set (COADS) has been used to show that 80% of the monthly mean air temperature variance is accounted for in regions of good data coverage. A detailed analysis of the sources of errors in in situ SSTs and an overall estimate of the noise are obtained from the COADS by assessing the variability within 2° longitude by 2° latitude boxes within each month for 1979. In regions of small spatial gradient of mean SST, individual SST measurements are representative of the monthly mean in a 2° box to within a standard error of 1.0°C in the tropics and 1.2° to 1.4°C in the extratropics. The standard error is larger in the North Pacific than in the North Atlantic and much larger in regions of strong SST gradient, such as within the vicinity of the Gulf Stream, because both within-month temporal variability and within-2° box spatial variability are enhanced. The total standard error of the monthly mean in each box is reduced approximately by the square root of the number of observations available. The overall noise in SSTs ranges from less than 0.1°C over the North Atlantic to over 0.5deg;C over the oceans south of about 35°S. Greater daily variability in surface marine air temperatures than in SSTs means that two to three times as many observations are needed per month to reduce the noise in the monthly mean air temperature to the same level as for SST. Tests of the reproducibility of SSTs in analyses from the U.K. Meteorological Office (UKMO) and the U.S. Climate Analysis Center (CAC) and from COADS reveal monthly anomaly correlations on a 5° grid exceeding 0.9 over the northern oceans but less than 0.6 in the central tropical Pacific and south of about 35°S. Root-mean-square differences between CAC and UKMO monthly SST anomalies exceed 0.6°C in the regions where the correlation is lower than about 0.6. With the marked exception of the eastern tropical Pacific, where the large El Niño signal is easily detected, there are insufficient numbers of SST observations to reliably define SST or surface air temperature monthly mean anomalies over most of the oceans south of about 10°N. The use of seasons rather than months can improve the signal-to-noise ratio if careful treatment of the annual cycle is included. For seasonal means, SST anomalies cannot be reliably defined south of 20°S in the eastern Pacific and south of ∼35°S elsewhere except near New Zealand. In light of the noise estimates and the much fewer numbers of observations in the past, difficulties in establishing temperatures from the historical record are discussed.
Trends in extreme 100-y events of temperature and rainfall amounts in the continental United States are estimated, to see effects of climate change. This is a nontrivial statistical problem because climate change effects have to be extracted from “noisy” weather data within a limited time range. We use nonparametric Bayesian methods to estimate the trends of extreme events that have occurred between 1979 and 2019, based on data for temperature and rainfall. We focus on 100-y events for each month in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>°</mml:mo> <mml:mo>×</mml:mo> <mml:mn>1</mml:mn> <mml:mo>°</mml:mo> </mml:mrow> </mml:math> geographical areas looking at hourly temperature and 5-d cumulative rainfall. Distribution tail models are constructed using extreme value theory (EVT) and data on 33-y events. This work shows it is possible to aggregate data from spatial points in diverse climate zones for a given month and fit an EVT model with the same parameters. This surprising result means there are enough extreme event data to see the trends in the 41-y record for each calendar month. The yearly trends of the risk of a 100-y high-temperature event show an average 2.1-fold increase over the last 41 y of data across all months, with a 2.6-fold increase for the months of July through October. The risk of high rainfall extremes increases in December and January 1.4-fold, but declines by 22% for the spring and summer months.
An update is given of the global correlation and regression patterns of sea level pressure associated with the Southern Oscillation, based upon the reanalyses from the National Centers for Environmental Prediction–National Center for Atmospheric Research for 1958–98, a period independent of that of early work. Features over the oceans are better defined than was previously possible and most features prove to be robust, although climate changes such as the 1976 climate shift have evidently altered some important relationships, such as those with Southeast Asia. Associated surface temperature patterns are also shown over the same interval and reveal striking symmetry about the equator. For El Niño, the patterns emphasize the associated broad warming over the tropical central and eastern Pacific, as well as along the west coast of the Americas extending into high latitudes of the Pacific in both hemispheres, and cooling in the central North and South Pacific. Precipitation patterns associated with the Southern Oscillation are given based upon the post-1979 period to include satellite data over the oceans, which emphasizes that the main changes are for a global redistribution of precipitation, so that solely land-based perspectives are biased. While annual mean patterns reveal much of the geographic structure associated with the Southern Oscillation, important seasonal variations are present, especially for sea level pressure and precipitation.
The main problem in weather forecasting over New Zealand and the Southern Oceans is the lack of sufficient data for good analyses, especially at upper levels. Numerical weather prediction also suffers from this problem, but it can to some degree be alleviated by producing several prognoses from reasonable alternative analyses to alert the forecaster to the sensitivity of the situation to data-blank areas. This procedure allows forecasters to assess the degree of confidence that can be placed in a forecast since unresolvable problems in the initial analyses cause uncertainty in predicting the exact timing and areas that will be affected by short-wave disturbances. It also gives them greater flexibility when later information becomes available and clearly invalidates one of the forecasts. The performance of a five-layer quasi-geostrophic model developed for this purpose in New Zealand is described and illustrated with one case study of an unusual summer situation in the Australia–New Zealand region where a decaying tropical cyclone is absorbed into the frontal system of an extratropical cyclone undergoing explosive cyclogenesis.
An analysis is made of the planetary-scale response of the atmosphere to the kinematic effects of orographic forcing by, in particular, the Tibetan Plateau-Himalayan Mountain complex. Theoretical scaling arguments are used to deduce a critical mountain height hc beyond which the component of flow around will dominate that over the orography. The hc is proportional to the meridional scale of the orography and depends on latitude, For north–south scales appropriate for the Himalayas hc ∼ 1.5 km which is much less than the actual height of 3706 m when resolved with tour zonal planetary waves, with the implication that the "around" component will dominate. A steady-state planetary wave model which has a full kinematic nonlinear lower boundary condition is used to simulate the response to the eastern orography whose height has been multiplied by factors ranging from 0.1 to 2.0. Although the mountain configuration was fixed, the locations of the simulated perturbation highs and lows change substantially in such a way that the total flow increasingly adjusts to go around the high orography as the mountain heights are increased. This effect limits the total vertical motion induced by the orography and thus the amplitudes of the forced planetary waves increase at a rate much less than expected from linear theory. Neglected nonlinear terms in the model are shown to be relatively small in all cases. For shallow mountains the maximum response occurs at the latitude of the mountain (35°N) but both the maximum response and the maximum zonal mean poleward heat flux by the simulated waves are shifted poleward to ∼55°N for orography >1500 m high, consistent with the observed location of the wintertime stationary waves in the Northern Hemisphere. Overall, results support the expectations from scaling considerations and show that linear theory may be reasonably applied for Himalayan orography up to ∼1 km high when resolved on planetary scales, but the "around" component dominates the "over" component when the orography exceeds 1.5 km, as is the case in actuality. The around component should also dominate for the Greenland plateau and Antarctica but the effects are more equivocal for the Rockies which are only ∼1 km high when resolved with planetary scales while hc ∼ 1.5 km.
No abstract is provided for this article.
No abstract is provided for this article.
Although uncertainties in global warming are many and varied, they are not as great as stated in the recent U.S. National Research Council (NRC) report ([1][1]). As Richard A. Kerr notes in his News of the Week article, President George W. Bush seized upon these uncertainties to justify the administration's limited response (“Bush backs spending for a ‘global problem,’” 15 Jun., p. [1978][2]). Specifically, Bush “emphasized that the contribution of natural climate variability to the past century's warming is uncertain,” to quote Kerr. Unfortunately, in the NRC report, two aspects of natural climate variability are conflated. First, there is natural variability that is tied to external forcings, such as variations in the Sun, volcanoes, and the orbital variations of Earth around the Sun. The latter is the driving force for the major ice ages and interglacial periods. Second, there is natural variability that is internal to the climate system, arising, for instance, from interactions between the atmosphere and ocean, such as El Nino. This internal variability occurs even in an unchanging climate. In the NRC report and in its summary, natural variability is said to be “quite large,” but both kinds of variability are treated as if they are internal. Glacial to interglacial swings are discussed without mention of the known causes. Several lines of evidence, from the instrumental and paleoclimate records ([2][3]) and from climate models ([3][4]), strongly suggest that the recent increase in global mean temperature is beyond that possible from internal processes and thus must be caused by an increase in heating. This reasoning also puts limits on how large aerosol cooling could be. Further, known causes such as changes in the Sun and volcanic activity in the past 50 years have, if anything, led to cooling in this interval, leaving only the human-caused increase in greenhouse gases as the culprit. This reasoning has also been quantitatively confirmed with climate models ([3][4], [4][5]). A consequence of mistreatment of natural climate variability in the NRC report is that the caveats are overstated. Natural climate variability is dealt with much more thoroughly in the recent Intergovernmental Panel on Climate Change (IPCC) assessment ([4][5]), which was developed over about 3 years (versus 1 month for the NRC report). The summary from the IPCC is that “[t]here is new and stronger evidence that most of the warming observed over the last 50 years is attributable to human activities.” 1. [↵][6]Committee on the Science of Climate Change, Division of Earth and Life Studies, National Research Council, Climate Change Science: An Analysis of Some Key Questions (National Academy Press, Washington, DC, 2001). Available at . 2. [↵][7]1. M. E. Mann, 2. R. S. Bradley, 3. M. K. Hughes , Geophys. Res. Lett. 26, 759 (1999). [OpenUrl][8][CrossRef][9][Web of Science][10] 3. [↵][11]1. P. A. Stott 2. et al. , Science 281, 2133 (2000). [OpenUrl][12] 4. [↵][13]IPCC, Climate Change 2001: The Scientific Basis, J. T. Houghton et al., Eds. (Cambridge Univ. Press, Cambridge, in press). [1]: #ref-1 [2]: /lookup/doi/10.1126/science.292.5524.1978 [3]: #ref-2 [4]: #ref-3 [5]: #ref-4 [6]: #xref-ref-1-1 View reference 1 in text [7]: #xref-ref-2-1 View reference 2 in text [8]: {openurl}?query=rft.jtitle%253DGeophys.%2BRes.%2BLett.%26rft.volume%253D26%26rft.spage%253D759%26rft_id%253Dinfo%253Adoi%252F10.1029%252F1999GL900070%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [9]: /lookup/external-ref?access_num=10.1029/1999GL900070&link_type=DOI [10]: /lookup/external-ref?access_num=000079207400034&link_type=ISI [11]: #xref-ref-3-1 View reference 3 in text [12]: {openurl}?query=rft.jtitle%253DScience%26rft.volume%253D281%26rft.spage%253D2133%26rft.atitle%253DSCIENCE%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [13]: #xref-ref-4-1 View reference 4 in text
A short nontechnical review of the El Niño–Southern Oscillation (ENSO) phenomenon and the associated teleconnections to higher latitudes is given. ENSO has been shown to be predictable to some degree for over a year ahead, which therefore provides a basis for skillful prediction of interannual variations in climate. The processes involved are emphasized in order to highlight the areas where future research may most profitably be directed to improve climate forecasts. This progress has been realized through the successful completion of the Tropical Oceans Global Atmosphere (TOGA) Program of the World Climate Research Program (WCRP), which has established and maintained the TOGA Observing System, developed coupled atmosphere–ocean models of the tropical Pacific Ocean, demonstrated the predictive capabilities noted above, and conducted field programs to further the understanding of physical processes. Future research will take place in the context of a developing infrastructure associated with operational climate forecasts. Short-term climate variability involves much more than ENSO, and the challenge is to also capitalize on long timescales associated with anomalies in other parts of the climate system and any additional predictability that goes beyond simple persistence and build this into any prediction scheme. The international framework for helping to facilitate future research is CLIVAR–GOALS, the WCRP program on climate variability and predictability, and the subprogram on the Global Ocean–Atmosphere–Land System. The research challenges are many, but the prospects are excellent for making further advances and the potential is high for socioeconomic benefits for many countries. Turning skillful but uncertain forecasts into useful information and beneficial decisions represents a particular challenge, so that collaboration will be required between the physical scientists and scientists from the applications and social science communities.
No abstract is provided for this article.
The current Earth's energy imbalance (EEI) is mostly caused by human activity, and is driving global warming. The absolute value of EEI represents the most fundamental metric defining the status of global climate change, and will be more useful than using global surface temperature. EEI can best be estimated from changes in ocean heat content, complemented by radiation measurements from space. Sustained observations from the Argo array of autonomous profiling floats and further development of the ocean observing system to sample the deep ocean, marginal seas and sea ice regions are crucial to refining future estimates of EEI. Combining multiple measurements in an optimal way holds considerable promise for estimating EEI and thus assessing the status of global climate change, improving climate syntheses and models, and testing the effectiveness of mitigation actions. Progress can be achieved with a concerted international effort.
