Publications

El Niño and climate change

A comprehensive statistical analysis of how an index of the Southern Oscillation changed from 1882 to 1995 was given by Trenberth and Hoar [1996], with a focus on the unusual nature of the 1990–1995 El Niño‐Southern Oscillation (ENSO) warm event in the context of an observed trend for more El Niño and fewer La Niña events after the late 1970s. The conclusions of that study have been challenged by two studies which deal with only the part of our results pertaining to the length of runs of anomalies of one sign in the Southern Oscillation Index. They therefore neglect the essence of Trenberth and Hoar, which focussed on the magnitude of anomalies for certain periods and showed that anomalies during both the post‐1976 and 1990‐mid‐1995 periods were highly unlikely given the previous record. With updated data through mid 1997, we have performed additional tests using a regression model with autoregressive‐moving average (ARMA) errors that simultaneously estimates the appropriate ARMA model to fit the data and assesses the statistical significance of how unusual the two periods of interest are. The mean SOI for the post‐1976 period is statistically different from the overall mean at <0.05% and so is the 1990‐mid‐1995 period. The recent evolution of ENSO, with a major new El Niño event underway in 1997, reinforces the evidence that the tendency for more El Niño and fewer La Niña events since the late 1970s is highly unusual and very unlikely to be accounted for solely by natural variability.

Warmer Oceans, Stronger Hurricanes

Evidence is mounting that global warming enhances a cyclone's damaging winds and flooding rains

Physical Climate Processes and Feedbacks

Contributing Authors R.B. Alley, O.E. Anisimov, C. Appenzeller, R.G. Barry, J.J. Bates, R. Bindschadler, G.B. Bonan, C.W. Boning, S. Bony, H. Bryden, M.A. Cane, J.A. Curry, T. Delworth, A.S. Denning, R.E. Dickinson, K. Echelmeyer, K. Emanuel, G. Flato, I. Fung, M. Geller, P.R. Gent, S.M. Griffies, I. Held, A. Henderson-Sellers, A.A.M. Holtslag, F. Hourdin, J.W. Hurrell, V.M. Kattsov, P.D. Killworth, Y. Kushnir, W.G. Large, M. Latif, P. Lemke, M.E. Mann, G. Meehl, U. Mikolajewicz, W. O’Hirok, C.L. Parkinson, A. Payne, A. Pitman, J. Polcher, I. Polyakov, V. Ramaswamy, P.J. Rasch, E.P. Salathe, C. Schar, R.W. Schmitt, T.G. Shepherd, B.J. Soden, R.W. Spencer, P. Taylor, A. Timmermann, K.Y. Vinnikov, M. Visbeck, S.E. Wijffels, M. Wild

Evaluation of NMC Global Analyses: 1979-1987

No abstract is provided for this article.

Global temperature rises in steps – here’s why we can expect a steep climb this year and next

Model-based evidence of deep-ocean heat uptake during surface-temperature hiatus periods

In some decades, such as 2000–2009, the observed globally averaged surface-temperature time series has shown a flat or slightly negative trend. A modelling study provides evidence that heat uptake by the deep ocean may cause these hiatus periods and may be linked to La Niña-like conditions. There have been decades, such as 2000–2009, when the observed globally averaged surface-temperature time series shows little increase or even a slightly negative trend1 (a hiatus period). However, the observed energy imbalance at the top-of-atmosphere for this recent decade indicates that a net energy flux into the climate system of about 1 W m−2 (refs 2, 3) should be producing warming somewhere in the system4,5. Here we analyse twenty-first-century climate-model simulations that maintain a consistent radiative imbalance at the top-of-atmosphere of about 1 W m−2 as observed for the past decade. Eight decades with a slightly negative global mean surface-temperature trend show that the ocean above 300 m takes up significantly less heat whereas the ocean below 300 m takes up significantly more, compared with non-hiatus decades. The model provides a plausible depiction of processes in the climate system causing the hiatus periods, and indicates that a hiatus period is a relatively common climate phenomenon and may be linked to La Niña-like conditions.

Intermittency in Precipitation: Duration, Frequency, Intensity, and Amounts Using Hourly Data

Intermittency is a core characteristic of precipitation, not well described by data and very poorly modeled. Detailed analyses are made of near-global gridded (about 1°) hourly or 3-hourly precipitation rates from two updated observational datasets [3-hourly TRMM 3B42, version 7, and hourly CMORPH, version 1.0, bias corrected (CRT)] and from special runs of CESM from January 1998 to December 2013 to obtain hourly values. The analyses explore the intermittency of precipitation: the frequency, intensity, duration, and amounts. A comparison is made for all products using several metrics with a focus on the duration of events, and a new metric is proposed based on the ratio of the frequency of precipitation at certain rates (0.2–2 mm h−1) for hourly versus 3-hourly versus daily data. For all seasons and rain rates, TRMM values are similar in pattern to CMORPH, but durations are about 80%–85%. It is mainly over land in the monsoons that CMORPH exceeds TRMM rain durations. Observed duration of precipitation events in CMORPH over oceans are 12–15 h in the tropics and subtropics, much less than the ~20 h for CESM. Hence, the observational results differ somewhat but both are considerably different from the model, which has too much precipitation overall, and it precipitates far too often at low rates and not enough for intense rates, with the divide about 1–2 mm h−1. There is a need to properly represent precipitation phenomena and processes either explicitly or implicitly (parameterized).

An Observational Estimate of Inferred Ocean Energy Divergence

Monthly net surface energy fluxes (FS) over the oceans are computed as residuals of the atmospheric energy budget using top-of-atmosphere (TOA) net radiation (RT) and the complete atmospheric energy (AE) budget tendency (δAE/δt) and divergence (∇ · FA). The focus is on TOA radiation from the Earth Radiation Budget Experiment (ERBE) (February 1985–April 1989) and the Clouds and Earth’s Radiant Energy System (CERES) (March 2000–May 2004) satellite observations combined with results from two atmospheric reanalyses and three ocean datasets that enable a comprehensive estimate of uncertainties. Surface energy flux departures from the annual mean and the implied annual cycle in “equivalent ocean energy content” are compared with the directly observed ocean energy content (OE) and tendency (δOE/δt) to reveal the inferred annual cycle of divergence (∇ · FO). In the extratropics, the surface flux dominates the ocean energy tendency, although it is supplemented by ocean Ekman transports that enhance the annual cycle in ocean heat content. In contrast, in the tropics, ocean dynamics dominate OE variations throughout the year in association with the annual cycle in surface wind stress and the North Equatorial Current. An analysis of the regional characteristics of the first joint empirical orthogonal function (EOF) of FS, δOE/δt, and ∇ · FO is presented, and the largest sources of uncertainty are attributed to variations in OE. The mean and annual cycle of zonal mean global ocean meridional heat transports are estimated. The annual cycle reveals the strongest poleward heat transports in each hemisphere in the cold season, from November to April in the north and from May to October in the south, with a substantial across-equatorial transport, exceeding 4 PW in some months. Annual mean results do not differ greatly from some earlier estimates, but the sources of uncertainty are exposed. Comparison of annual means with direct ocean observations gives reasonable agreement, except in the North Atlantic, where transports from the ocean transects are slightly greater than the estimates presented here.

HESS Opinions "A perspective on isotope versus non-isotope approaches to determine the contribution of transpiration to total evaporation"

. Current techniques to disentangle the evaporative fluxes from the continental surface into a contribution evaporated from soils and canopy, or transpired by plants, are under debate. Many isotope-based studies show that transpiration contributes generally more than 70% to the total evaporation, while other isotope-independent techniques lead to considerably smaller transpiration fractions. This paper provides a perspective on isotope-based versus non-isotope-based partitioning studies. Some partitioning results from isotope-based methods, hydrometric measurements, and modeling are presented for comparison. Moreover, the methodological aspects of the partitioning analysis are considered, including their limitations, and explanations of possible discrepancies between the methods are discussed. We suggest sources of systematic error that may lead to biases in the results, e.g., instruments inaccuracy, assumptions used in analyses, and calibration parameters. A number of comparison studies using isotope-based methods and hydrometric measurements in the same plants and climatic conditions are consistent within the errors; however, models tend to produce lower transpiration fractions. The relatively low transpiration fraction in current state-of-the-art land-surface models calls for a reassessment of the skill of the underlying model parameterizations. The scarcity of global evaporation data makes calibration and validation of global isotope-independent and isotope-based results difficult. However, isotope-enabled land-surface and global climate modeling studies allow for the evaluation of the parameterization of land-surface models by comparing the computed water isotopologue signals in the atmosphere with the available remote sensing and flux-based data sets. Future studies that allow for this evaluation could provide a better understanding of the hydrological cycle in vegetated regions.

Satellite versus Surface Estimates of Air Temperature since 1979

A comparison of near-global monthly mean surface temperature anomalies to those of global Microwave Sounding Unit (MSU) 2R temperatures for 1979–95 reveals differences in global annual mean trends that are shown to be largely attributable to important physical differences in the quantities that are measured. Maps of standard deviations of the monthly mean anomalies, which can be viewed as mostly measuring the size of the climate signal, reveal pronounced differences regionally in each dataset. At the surface, the variability of temperatures is relatively small over the oceans but large over land, whereas in the MSU record the signal is much more zonally symmetric. The largest differences are found over the North Pacific and North Atlantic Oceans where the monthly standard deviations of the MSU temperatures are larger by more than a factor of 2. Locally over land, the variance of the surface record is larger than that of the MSU. In addition to differential responses to forcings from the El Niño-Southern Oscillation phenomenon and volcanic eruptions, these characteristics are indicative of differences of the response to physical processes arising from the relative importance of advection versus surface interactions and the different heat capacities of land and ocean. The result is that the regions contributing to hemispheric or global mean anomalies differ substantially between the two temperature datasets. This helps to account for the observed differences in decadal trends where the surface record shows a warming trend since 1979 of 0.18°C per decade, relative to the MSU record. While a common perception from this result is that the MSU and surface measurements of global temperature change are inconsistent, the issue should not be about which record is better, but rather that both give a different perspective on the same events.

Review comments

This paper addresses the issue of residence time of atmospheric moisture and concludes that the value originally proposed by Trenberth (1998) of 8.9 days still applies.It provides a partial commentary on an earlier paper by Läderach and Sodemann (2016) which suggested that the residence time was less than half, namely 3.9 days.Two methods are used to assess the lifetime and age of moisture in the atmosphere and the basic fields used come from ERA-Interim reanalyses.

Effects of Missing Data on Estimates of Monthly Mean General Circulation Statistics

To assess the impact of missing data on general circulation statistics, an analysis has been carried out on the effect of systematically decreasing the amount of available data. Thus the accuracy of monthly mean analyses of a variety of parameters including means, variances, and covariances is determined from ECMWF data as a function of the number of twice-daily analyses included in the monthly mean. Because the standard error of a monthly mean depends on the standard deviation of the daily values and the effective number of independent observations during each month, results have been expressed as the ratio of the root-mean-square error (RMSE) in the monthly mean to the daily standard deviation, thus allowing fairly universal relationships to be developed for application to many parameters, and to different season latitudes and longitudes. Results are indeed dependent upon the numbers of observations and the autocorrelation within each series, but could be modeled sufficiently well, for this purpose, with a first order autoregressive (Markov) process to allow simulated data to be used for further tests. Experiments have been carried out varying the number of observations missing that were 1) evenly spaced, 2) randomly spaced, or 3) occurring in single blocks. If missing observations am randomly spaced, the RMSE increases by factors of 2–3 over equally spaced data and there is virtually no advantage due to autocorrelation in the data. If the missing data occur in one block, another increase in RMSE occurs by a factor up to 2. The zonal mean daily standard deviations for the horizontal wind components, vertical p-velocity ω geopotential height, and temperature are presented for January. Tables are given of the ratio of the RMSE of the monthly mean to the daily standard deviation, and along with the autocorrelation, these allow estimates of either the relative or absolute errors to be expected in monthly mean statistics. For more persistent variables, fewer observations per month are needed to accurately define a monthly mean. Consequently, for variables such as ω, in which the persistence in the series is weak up to twice as many observations are needed to bring about the same RMSE ratio as for other linear variables. The RMSE ratios for variances and covariances are only slightly greater than those obtained for highly correlated linear variables such as the wind components u and v. As an example, for the zonal wind component at 300 hPa, 11 observations equally distributed in time would produce a RMSE in the monthly mean wind of ∼2.4 m s−1. If the same number of observations were randomly distributed, the RMSE increases to 4.1 m s−1; or if 20% of the observations were missing in one block, the RMSE would be 2.4 m s−1. For the monthly mean poleward momentum flux by the transient eddies the expected RMSE would be up to 27 m2 s−2 for 11 randomly distributed observations.

Near-global covariability of hourly precipitation in space and time.

A detailed analysis of hourly precipitation from 60°N to 60°S for the covariability is performed at 0.25° resolution using the new CMORPH dataset. For all points, correlations are computed ...

An Evaluation of Monthly Mean MSU and ECMWF Global Atmospheric Temperatures for Monitoring Climate

Monthly mean brightness temperature anomalies derived from channel 2 of the microwave sounding units (M5Us) on board NOAA satellites over the past decade are examined and compared with both weighted and pressure-level ECMWF monthly mean temperatures for the 96 months of 1982–89. Very good agreement between the MSU and channel 2 weighted ECMWF anomalies is found over most of the globe with correlation coefficients over 0.9, but the agreement falls off over the tropics, the South Atlantic, and high latitudes of the Southern Hemisphere. The ECMWF analyses agree best with the MSU data in regions of good radiosonde coverage, while lower correlations are found in regions where the analyses depend more heavily on satellite data. Systematic errors introduced into the analyses by the retrieval techniques applied to the radiance data largely explain this apparent contradiction. Additionally, changes to the analysis-forecast system at ECMWF over the decade appear as apparent changes in climate, and these discontinuities most strongly affect the tropics and are evident in regions of fewer observations. To the extent that the weighted ECMWF data data with the MSU brightness temperatures the vertical dependence of the MSU data can be examined. Correlations of the MSU data with ECMWF temperature anomalies at individual pressure levels are highest at 300 mb over the globe, a level apparently less affected by the frequent changes and improvements at ECMWF. Over regions of good data coverage, such as the Northern Hemisphere landmasses and Australia. the MSU anomalies correlate very highly with all levels of the troposphere up to 200 mb. Thus, the MSUs appear to be an extremely useful tool for measuring global tropospheric temperature fluctuations on a monthly and longer time scale. Problems in the ECMWF temperature record since 1982 am examined in detail for the tropics. In September 1982 the introduction of diabatic nonlinear normal-mode initialization resulted in significant temperature increases in the tropical middle troposphere, especially at 500 mb. In May 1985 tropical temperatures at 700 mb (950 inb) increased (decreased) after the implementation of the T106 spectral model with major accompanying changes to physical parameterizations. Tropical temperatures near the tropopause decreased substantially after the May 1986 enhancement of the vertical revolution of the model from 16 to 19 levels with 3 new stratosphere levels. Problems at 1000 mb are present throughout the 96-month study period and are directly related to the manner in which analyzed temperatures are obtained at ECMWF. Comparisons with temperatures obtained from radiosonde stations in the tropics show that the ECMWF analyses have clearly improved with time, especially after May 1985 after which the 1000–200-mb temperatures show much greater coherence. These results show the importance of realizing the inherent problems with operationally based gridded datasets, and they strongly support the need for reanalysis of all data using a state-of-the-art four-dimensional data assimilation system.

Toward Integrated Reconstruction of Past Climates

Reconstructions of past climates rely to a large extent on the difficult interpretation of climate proxies such as tree rings, ice cores, and sediments. In their Perspective, Trenberth and Otto-Bliesner argue that important issues such as the seasonality of past climate and changes in large-scale climate patterns such as the El NiA±o-Southern Oscillation (ENSO) can only be resolved if climate modelers and paleoclimate experts work closely together. They warn that conclusions about changes in ENSO from records that do not resolve interannual variability are especially fraught with difficulty.

Yes, we can do ‘sound’ climate science even though it’s projecting the future

Global Sea Surface Temperature Analyses: Multiple Problemsand Their Implications for Climate Analysis, Modeling, and Reanalysis

A comprehensive comparison is made among four sea surface temperature (SST) datasets: the optimum interpolation (OI) and the empirical orthogonal function reconstructed SST analyses from the National Centers for Environmental Prediction (NCEP), the Global Sea-Ice and SST dataset (GISST, version 2.3b) from the United Kingdom Meteorological Office, and the optimal smoothing SST analysis from the Lamont-Doherty Earth Observatory (LDEO). Significant differences exist between the GISST and NCEP 1961–90 SST climatologies, especially in the marginal sea-ice zones and in regions of important small-scale features, such as the Gulf Stream, which are better resolved by the NCEP product. Significant differences also exist in the SST anomalies that relate strongly to the number of in situ observations available. In recent years, correlations between monthly anomalies are less than 0.75 south of about 10°N and are lower still over the southern oceans and parts of the tropical Pacific where root-mean-square differences exceed 0.6°C. While adequate for many purposes, the SST datasets all contain problems of one sort or another. Noise is evident in the GISST data and realistic temporal persistence of SST anomalies after 1981 is lacking. Trends in recent years are quite different between the GISST and NCEP analyses, and this can be partially traced to differences in the processing of in situ data and an increasing cold bias in the NCEP 01 data arising from incompletely corrected satellite data. Significant discrepancies also exist in centennial trends from the LDEO and GISST datasets, and these likely reflect the separate treatment of the very low frequency signal in the GISST analysis and questionable assumptions about the stationarity of statistics in the LDEO method. Ensembles of integrations with an atmospheric general circulation model (AGCM) are used with three of the SST datasets as lower boundary conditions to show that the differences among them imply physically important differences in the atmospheric circulation. Over the Tropics, where masking by internal atmospheric variability is small, SST differences affect moist convection and systematically produce strong responses in the local divergent circulation. A case study shows that analyzed SST differences in the tropical Pacific can be as large as for a moderate El Niño. Such large discrepancies induce local rainfall anomalies up to 8 mm day−1 and, in addition to the tropical circulation anomalies, are associated with global teleconnections that influence temperatures and precipitation around the world. Results also show the limitations to using AGCMs when forced by specified SSTs. The likely sources of the problems evident in the different SST products are identified and discussed. Several of the problems are being addressed by current efforts to reprocess the SST data, which is strongly recommended, but remaining problems demand further attention and attempts to resolve them should continue. The choice among SST analyses used for AGCM simulations, for the atmospheric reanalysis projects, for identifying climate signals, and for monitoring climate is important, as known flaws in the global analyses can compromise the results.

In 50 years, will global warming have had any positive effects on Americans?

No abstract is provided for this article.

The 2001 Assessment of Climate Change