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.
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 ...
Potential predictability of a meteorological time series can be estimated from the ratio of the actual interannual variability to the natural variability associated with climatic noise. The extent to which this ratio is larger than one is taken as a measure of the climatic signal-to-noise variance ratio. However, there are major problems in separating out the signal from the noise which are compounded by persistence in the time series, the presence of an annual cycle and the effects of finite sample size. An F test may be used to deduce that a signal is present by rejecting the null hypothesis of no signal. However, it is shown that, generally, the null hypothesis should not be accepted just because it cannot be rejected. Confidence limits can be very large when a signal is present in a finite sample since the signal will be fictitiously correlated with the noise due to sampling in a manner that is unknown. Although the correlation coefficient is statistically not significant, there is a large impact on the confidence limits of the F ratio. A case is presented using many artificially generated, and thus known, time series that include a signalcombined with a first order autoregressive process (red noise). With a signal-to-noise ratio of 1 (<F) = 2) only about half of the time could the null hypothesis be rejected at the 5% level and 5% of the time the sample F ratio was found to be less than I. The problem of assessing potential predictability is analyzed in detail in the time domain. The presence of any low frequency signal can lead to overestimates of the level of climatic noise since the signal will add to the persistence of the time series. A method is devised to adjust the statistics and remove the effects of the signal and thus obtain a more accurate signal-to-noise ratio. The results are compared with the alternative approach of Jones and of Madden in the frequency domain. The latter uses a low frequency white noise extension of the power spectrum to estimate the climatic noise. The method is shown to work quite well, with minimal impact of a low frequency signal on results, but the confidence limits were very large. It is necessary to greatly increase the degrees of freedom of the spectral estimates before the results can be considered reliable; even then they are still subject to the inherent uncertainty associated with the unknown correlation between signal and noise.
The concept of dividing the year into four seasons is reexamined to appraise critically the relative merit of two commonly used definitions of the seasons: 1) the astronomical definition; and 2) the meteorological breakdown into four three-month periods. These are compared with the definition of winter as the coldest season, summer as the warmest season, and spring and autumn as the transition seasons. Observational data on surface temperatures over the entire globe and, in particular, over the United States, are used to determine what the seasons should be. Presented here is an analysis of the amplitude, and phase of and percentage variance explained by the first harmonic of solar radiation at the top of the atmosphere and surface temperatures. Annual changes in surface temperature associated with the seasons are much larger over land than over the oceans. Surface temperatures lag the solar cycle by 27½ days over the United States, compared with 32½ days in mid-latitudes over the Northern Hemisphere as a whole, and 44 days in mid-latitudes of the Southern Hemisphere. The astronomical definition of seasons is appropriate only over the oceanic regions of the Southern Hemisphere. Over the continental regions of the Northern Hemisphere, the "meteorological" seasons in which winter is December, January, and February, etc., agree reasonably well with observed events and are recommended for general usage.
Two global data sets have been analyzed to determine the interannual variability of the zonal mean mass distribution. The first is a National Meteorological Center (NMC) global data set of sea level pressures beginning in December 1977 and surface pressures beginning in April 1979. The second is an 11‐year (1972–1982) hybrid set of sea level pressures from the Australian southern hemisphere and U.S. Navy northern hemisphere analyses. The two sets are compared and evaluated by seeing how well they satisfy the constraint of conservation of mass. Anomalies in total mass due to changes in water vapor content of the atmosphere are lost in the data problems. Several inhomogeneities or discontinuities are found and are identified either with changes in analysis procedures in both data sets or the introduction of new orography and operational models at NMC. Consequently only limited periods of data are suitable for determining the dominant modes of variability. Nevertheless, hemispheric mean anomalies in mass in one hemisphere tend to be mirrored in the other, and it is clear that previously deduced fluctuations in hemispheric mean mass are mostly real. Predominant modes of variability of zonal means reveal systematic exchanges of mass (1) almost entirely confined within each hemisphere in isolation, (2) between the extratropics of each hemisphere with little impact on the tropics as an antisymmetric mode, and (3) between the tropics and extratropics in a symmetric mode. The second mode played a strong role in redistributing mass during April–July of 1979 during the First GARP Global Experiment (FGGE) and resulted in an exceptionally deep circumpolar trough over the southern hemisphere and record high pressures over the northern hemisphere. The possible origin of these modes is briefly considered.
An update is provided on the Earth's global annual mean energy budget in the light of new observations and analyses. In 1997, Kiehl and Trenberth provided a review of past estimates and performed a number of radiative computations to better establish the role of clouds and various greenhouse gases in the overall radiative energy flows, with top-of-atmosphere (TOA) values constrained by Earth Radiation Budget Experiment values from 1985 to 1989, when the TOA values were approximately in balance. The Clouds and the Earth's Radiant Energy System (CERES) measurements from March 2000 to May 2004 are used at TOA but adjusted to an estimated imbalance from the enhanced greenhouse effect of 0.9 W m−2. Revised estimates of surface turbulent fluxes are made based on various sources. The partitioning of solar radiation in the atmosphere is based in part on the International Satellite Cloud Climatology Project (ISCCP) FD computations that utilize the global ISCCP cloud data every 3 h, and also accounts for increased atmospheric absorption by water vapor and aerosols. Surface upward longwave radiation is adjusted to account for spatial and temporal variability. A lack of closure in the energy balance at the surface is accommodated by making modest changes to surface fluxes, with the downward longwave radiation as the main residual to ensure a balance. Values are also presented for the land and ocean domains that include a net transport of energy from ocean to land of 2.2 petawatts (PW) of which 3.2 PW is from moisture (latent energy) transport, while net dry static energy transport is from land to ocean. Evaluations of atmospheric reanalyses reveal substantial biases.
How much of a disruption do we cause? The much-awaited answer could be ours by 2050, but only if nations of the world commit to long-term climate monitoring now
No abstract is provided for this article.
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.
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.
A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
No abstract is provided for this article.
No abstract is provided for this article.
This book has addressed the energy flows through the climate system. It has made the point that the total energy generation by humans is relatively small compared with natural flows, and the main way humans cause climate change is by interfering with the natural energy flows. Ironically, that interference comes mainly from our use of energy and the associated emissions of greenhouse gases and aerosols. In this case, “energy” encapsulates electricity, heat, transport, industrial, and agricultural activities. The latter include changes in land use. The primary source of the errant emissions is the burning of fossil fuels, generating carbon dioxide. A key issue then is how to decarbonize energy systems.
Variations in the hydrological cycle and the water vapor content of the atmosphere form a vital part of the “greenhouse” feedback mechanism that can substantially enhance perturbations in the atmosphere arising from changes in forcing, such as those associated with increases in the carbon dioxide content of the atmosphere. An analysis is therefore made of the mean annual cycle, interannual variability and trends in global‐scale water vapor content of the atmosphere for December 1978 through December 1985, using global analyses from the European Centre for Medium Range Weather Forecasts. Since the variations in total atmospheric mass are almost entirely due to water vapor, two entirely independent global measures of the water vapor content can be compared. These are the total surface pressure due to water vapor from humidity analyses and the total surface pressure itself. For the mean annual cycle these show excellent agreement, and the analyses are therefore compatible with the constraint that the total mass of dry air is conserved. However, it appears that both the interannual variability and trends in water vapor are sufficiently small that they are lost in the noise level of the data. A new estimate of the total mass of the atmosphere is 5.1361×10 18 kg for the annual mean, corresponding to a mean surface pressure of 984.43 mbar. It ranges from 5.1352×10 18 kg in January to 5.1371×10 18 kg in July, owing to the annual cycle in global water vapor which has an amplitude of 1.0×10 15 kg (0.2 mbar). The total mass of dry air is estimated to be 5.123×10 18 kg (or 981.9 mbar). Also presented is the partitioning of the mass for both the total and the water vapor into the contributions from each hemisphere and as a function of latitude. Mean annual surface pressures in the northern and southern hemispheres are found to be 981.92 and 986.93 mbar, respectively. Monthly mean hemispheric fluctuations in surface pressures of ±1 mbar are not uncommon and tend to be reflected by opposite anomalies in the other hemisphere (consistent with conservation of mass), but the residual in the global monthly mean is of the order of 0.1 mbar. Since this is greater than the magnitude of the possible signal in water vapor surface pressure, the available evidence indicates that the analyzed global monthly anomalies are mostly noise.
