American Geophysical Union (AGU)

We present the Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1° latitude‐longitude grid from 1871. The companion HadMAT1 runs monthly from 1856 on a 5° latitude‐longitude grid and incorporates new corrections for the effect on NMAT of increasing deck (and hence measurement) heights. HadISST1 and HadMAT1 temperatures are reconstructed using a two‐stage reduced‐space optimal interpolation procedure, followed by superposition of quality‐improved gridded observations onto the reconstructions to restore local detail. The sea ice fields are made more homogeneous by compensating satellite microwave‐based sea ice concentrations for the impact of surface melt effects on retrievals in the Arctic and for algorithm deficiencies in the Antarctic and by making the historical in situ concentrations consistent with the satellite data. SSTs near sea ice are estimated using statistical relationships between SST and sea ice concentration. HadISST1 compares well with other published analyses, capturing trends in global, hemispheric, and regional SST well, containing SST fields with more uniform variance through time and better month‐to‐month persistence than those in GISST. HadMAT1 is more consistent with SST and with collocated land surface air temperatures than previous NMAT data sets.

A rapid and accurate radiative transfer model (RRTM) for climate applications has been developed and the results extensively evaluated. The current version of RRTM calculates fluxes and cooling rates for the longwave spectral region (10–3000 cm⁻¹) for an arbitrary clear atmosphere. The molecular species treated in the model are water vapor, carbon dioxide, ozone, methane, nitrous oxide, and the common halocarbons. The radiative transfer in RRTM is performed using the correlated‐k method: the k distributions are attained directly from the LBLRTM line‐by‐line model, which connects the absorption coefficients used by RRTM to high‐resolution radiance validations done with observations. Refined methods have been developed for treating bands containing gases with overlapping absorption, for the determination of values of the Planck function appropriate for use in the correlated‐k approach, and for the inclusion of minor absorbing species in a band. The flux and cooling rate results of RRTM are linked to measurement through the use of LBLRTM, which has been substantially validated with observations. Validations of RRTM using LBLRTM have been performed for the midlatitude summer, tropical, midlatitude winter, subarctic winter, and four atmospheres from the Spectral Radiance Experiment campaign. On the basis of these validations the longwave accuracy of RRTM for any atmosphere is as follows: 0.6 W m⁻² (relative to LBLRTM) for net flux in each band at all altitudes, with a total (10–3000 cm⁻¹) error of less than 1.0 W m⁻² at any altitude; 0.07 K d⁻¹ for total cooling rate error in the troposphere and lower stratosphere, and 0.75 K d⁻¹ in the upper stratosphere and above. Other comparisons have been performed on RRTM using LBLRTM to gauge its sensitivity to changes in the abundance of specific species, including the halocarbons and carbon dioxide. The radiative forcing due to doubling the concentration of carbon dioxide is attained with an accuracy of 0.24 W m⁻², an error of less than 5%. The speed of execution of RRTM compares favorably with that of other rapid radiation models, indicating that the model is suitable for use in general circulation models.

A diagram has been devised that can provide a concise statistical summary of how well patterns match each other in terms of their correlation, their root‐mean‐square difference, and the ratio of their variances. Although the form of this diagram is general, it is especially useful in evaluating complex models, such as those used to study geophysical phenomena. Examples are given showing that the diagram can be used to summarize the relative merits of a collection of different models or to track changes in performance of a model as it is modified. Methods are suggested for indicating on these diagrams the statistical significance of apparent differences and the degree to which observational uncertainty and unforced internal variability limit the expected agreement between model‐simulated and observed behaviors. The geometric relationship between the statistics plotted on the diagram also provides some guidance for devising skill scores that appropriately weight among the various measures of pattern correspondence.

Relationships between wind speed and gas transfer, combined with knowledge of the partial pressure difference of CO₂across the air‐sea interface are frequently used to determine the CO₂flux between the ocean and the atmosphere. Little attention has been paid to the influence of variability in wind speed on the calculated gas transfer velocities and the possibility of chemical enhancement of CO₂exchange at low wind speeds over the ocean. The effect of these parameters is illustrated using a quadratic dependence of gas exchange on wind speed which is fit through gas transfer velocities over the ocean determined by the natural‐¹⁴C disequilibrium and the bomb‐¹⁴C inventory methods. Some of the variability between different data sets can be accounted for by the suggested mechanisms, but much of the variation appears due to other causes. Possible causes for the large difference between two frequently used relationships between gas transfer and wind speed are discussed. To determine fluxes of gases other than CO₂across the air‐water interface, the relevant expressions for gas transfer, and the temperature and salinity dependence of the Schmidt number and solubility of several gases of environmental interest are included in an appendix.

A primary component of the observed recent climate change is the radiative forcing from increased concentrations of long‐lived greenhouse gases (LLGHGs). Effective simulation of anthropogenic climate change by general circulation models (GCMs) is strongly dependent on the accurate representation of radiative processes associated with water vapor, ozone, and LLGHGs. In the context of the increasing application of the Atmospheric and Environmental Research, Inc. (AER), radiation models within the GCM community, their capability to calculate longwave and shortwave radiative forcing for clear sky scenarios previously examined by the radiative transfer model intercomparison project (RTMIP) is presented. Forcing calculations with the AER line‐by‐line (LBL) models are very consistent with the RTMIP line‐by‐line results in the longwave and shortwave. The AER broadband models, in all but one case, calculate longwave forcings within a range of −0.20 to 0.23 W m⁻² of LBL calculations and shortwave forcings within a range of −0.16 to 0.38 W m⁻² of LBL results. These models also perform well at the surface, which RTMIP identified as a level at which GCM radiation models have particular difficulty reproducing LBL fluxes. Heating profile perturbations calculated by the broadband models generally reproduce high‐resolution calculations within a few hundredths K d⁻¹ in the troposphere and within 0.15 K d⁻¹ in the peak stratospheric heating near 1 hPa. In most cases, the AER broadband models provide radiative forcing results that are in closer agreement with high‐resolution calculations than the GCM radiation codes examined by RTMIP, which supports the application of the AER models to climate change research.

Numerical assessments of global air quality and potential changes in atmospheric chemical constituents require estimates of the surface fluxes of a variety of trace gas species. We have developed a global model to estimate emissions of volatile organic compounds from natural sources (NVOC). Methane is not considered here and has been reviewed in detail elsewhere. The model has a highly resolved spatial grid (0.5°×0.5° latitude/longitude) and generates hourly average emission estimates. Chemical species are grouped into four categories: isoprene, monoterpenes, other reactive VOC (ORVOC), and other VOC (OVOC). NVOC emissions from oceans are estimated as a function of geophysical variables from a general circulation model and ocean color satellite data. Emissions from plant foliage are estimated from ecosystem specific biomass and emission factors and algorithms describing light and temperature dependence of NVOC emissions. Foliar density estimates are based on climatic variables and satellite data. Temporal variations in the model are driven by monthly estimates of biomass and temperature and hourly light estimates. The annual global VOC flux is estimated to be 1150 Tg C, composed of 44% isoprene, 11% monoterpenes, 22.5% other reactive VOC, and 22.5% other VOC. Large uncertainties exist for each of these estimates and particularly for compounds other than isoprene and monoterpenes. Tropical woodlands (rain forest, seasonal, drought‐deciduous, and savanna) contribute about half of all global natural VOC emissions. Croplands, shrublands and other woodlands contribute 10–20% apiece. Isoprene emissions calculated for temperate regions are as much as a factor of 5 higher than previous estimates.

A third‐generation numerical wave model to compute random, short‐crested waves in coastal regions with shallow water and ambient currents (Simulating Waves Nearshore (SWAN)) has been developed, implemented, and validated. The model is based on a Eulerian formulation of the discrete spectral balance of action density that accounts for refractive propagation over arbitrary bathymetry and current fields. It is driven by boundary conditions and local winds. As in other third‐generation wave models, the processes of wind generation, whitecapping, quadruplet wave‐wave interactions, and bottom dissipation are represented explicitly. In SWAN, triad wave‐wave interactions and depth‐induced wave breaking are added. In contrast to other third‐generation wave models, the numerical propagation scheme is implicit, which implies that the computations are more economic in shallow water. The model results agree well with analytical solutions, laboratory observations, and (generalized) field observations.

A suite of climate change indices derived from daily temperature and precipitation data, with a primary focus on extreme events, were computed and analyzed. By setting an exact formula for each index and using specially designed software, analyses done in different countries have been combined seamlessly. This has enabled the presentation of the most up‐to‐date and comprehensive global picture of trends in extreme temperature and precipitation indices using results from a number of workshops held in data‐sparse regions and high‐quality station data supplied by numerous scientists world wide. Seasonal and annual indices for the period 1951–2003 were gridded. Trends in the gridded fields were computed and tested for statistical significance. Results showed widespread significant changes in temperature extremes associated with warming, especially for those indices derived from daily minimum temperature. Over 70% of the global land area sampled showed a significant decrease in the annual occurrence of cold nights and a significant increase in the annual occurrence of warm nights. Some regions experienced a more than doubling of these indices. This implies a positive shift in the distribution of daily minimum temperature throughout the globe. Daily maximum temperature indices showed similar changes but with smaller magnitudes. Precipitation changes showed a widespread and significant increase, but the changes are much less spatially coherent compared with temperature change. Probability distributions of indices derived from approximately 200 temperature and 600 precipitation stations, with near‐complete data for 1901–2003 and covering a very large region of the Northern Hemisphere midlatitudes (and parts of Australia for precipitation) were analyzed for the periods 1901–1950, 1951–1978 and 1979–2003. Results indicate a significant warming throughout the 20th century. Differences in temperature indices distributions are particularly pronounced between the most recent two periods and for those indices related to minimum temperature. An analysis of those indices for which seasonal time series are available shows that these changes occur for all seasons although they are generally least pronounced for September to November. Precipitation indices show a tendency toward wetter conditions throughout the 20th century.

Recently reported radioisotopic dates and magnetic anomaly spacings have made it evident that modification is required for the age calibrations for the geomagnetic polarity timescale of Cande and Kent (1992) at the Cretaceous/Paleogene boundary and in the Pliocene. An adjusted geomagnetic reversal chronology for the Late Cretaceous and Cenozoic is presented that is consistent with astrochronology in the Pleistocene and Pliocene and with a new timescale for the Mesozoic.

The nearly coincident forms of the relations between seismic moment M₀ and the magnitudes M_L, M_S, and M_w imply a moment magnitude scale M = ⅔ log M₀ ‐ 10.7 which is uniformly valid for 3 ≲ M_L ≲ 7, 5 ≲ M_s ≲ 7½, and M_w ≳ 7½.