Nick Rayner

An Improved In Situ and Satellite SST Analysis for Climate

Abstract A weekly 1° spatial resolution optimum interpolation (OI) sea surface temperature (SST) analysis has been produced at the National Oceanic and Atmospheric Administration (NOAA) using both in situ and satellite data from November 1981 to the present. The weekly product has been available since 1993 and is widely used for weather and climate monitoring and forecasting. Errors in the satellite bias correction and the sea ice to SST conversion algorithm are discussed, and then an improved version of the OI analysis is developed. The changes result in a modest reduction in the satellite bias that leaves small global residual biases of roughly −0.03°C. The major improvement in the analysis occurs at high latitudes due to the new sea ice algorithm where local differences between the old and new analysis can exceed 1°C. Comparisons with other SST products are needed to determine the consistency of the OI. These comparisons show that the differences among products occur on large time- and space scales wit...

Improved Analyses of Changes and Uncertainties in Sea Surface Temperature Measured In Situ since the Mid-Nineteenth Century: The HadSST2 Dataset

Abstract A new flexible gridded dataset of sea surface temperature (SST) since 1850 is presented and its uncertainties are quantified. This analysis [the Second Hadley Centre Sea Surface Temperature dataset (HadSST2)] is based on data contained within the recently created International Comprehensive Ocean–Atmosphere Data Set (ICOADS) database and so is superior in geographical coverage to previous datasets and has smaller uncertainties. Issues arising when analyzing a database of observations measured from very different platforms and drawn from many different countries with different measurement practices are introduced. Improved bias corrections are applied to the data to account for changes in measurement conditions through time. A detailed analysis of uncertainties in these corrections is included by exploring assumptions made in their construction and producing multiple versions using a Monte Carlo method. An assessment of total uncertainty in each gridded average is obtained by combining these bias-...

Climate forcings in Goddard Institute for Space Studies SI2000 simulations

[1] We define the radiative forcings used in climate simulations with the SI2000 version of the Goddard Institute for Space Studies (GISS) global climate model. These include temporal variations of well-mixed greenhouse gases, stratospheric aerosols, solar irradiance, ozone, stratospheric water vapor, and tropospheric aerosols. Our illustrations focus on the period 1951–2050, but we make the full data sets available for those forcings for which we have earlier data. We illustrate the global response to these forcings for the SI2000 model with specified sea surface temperature and with a simple Q-flux ocean, thus helping to characterize the efficacy of each forcing. The model yields good agreement with observed global temperature change and heat storage in the ocean. This agreement does not yield an improved assessment of climate sensitivity or a confirmation of the net climate forcing because of possible compensations with opposite changes of these quantities. Nevertheless, the results imply that observed global temperature change during the past 50 years is primarily a response to radiative forcings. It is also inferred that the planet is now out of radiation balance by 0.5 to 1 W/m 2 and that additional global warming of about 0.5� C is already ‘‘in the pipeline.’’ INDEX TERMS: 1620 Global Change: Climate dynamics (3309); 1635 Global Change: Oceans (4203); 1650 Global Change: Solar variability;

Global temperature change and its uncertainties since 1861

We present the first analysis of global and hemispheric surface warming trends that attempts to quantify the major sources of uncertainty. We calculate global and hemispheric annual temperature anomalies by combining land surface air temperature and sea surface temperature (SST) through an optimal averaging technique. The technique allows estimation of uncertainties in the annual anomalies resulting from data gaps and random errors. We add independent uncertainties due to urbanisation, changing land-based observing practices and SST bias corrections. We test the accuracy of the SST bias corrections, which represent the largest source of uncertainty in the data, through a suite of climate model simulations. These indicate that the corrections are likely to be fairly accurate on an annual average and on large space scales. Allowing for serial correlation and annual uncertainties, the best linear fit to annual global surface temperature gives an increase of 0.61 ± 0.16°C between 1861 and 2000.

Adjusting for sampling density in grid-box land and ocean surface temperature time series

We develop methods for adjusting grid box average temperature time series for the effects on variance of changing numbers of contributing data. Owing to the different sampling characteristics of the data, we use different techniques over land and ocean. The result is to damp average temperature anomalies over a grid box by an amount inversely related to the number of contributing stations or observations. Variance corrections influence all grid box time series but have their greatest effects over data sparse oceanic regions. After adjustment, the grid box land and ocean surface temperature data sets are unaffected by artificial variance changes which might affect, in particular, the results of analyses of the incidence of extreme values. We combine the adjusted land surface air temperature and sea surface temperature data sets and apply a limited spatial interpolation. The effects of our procedures on hemispheric and global temperature anomaly series are small.

An Observationally Based Estimate of the Climate Sensitivity

Abstract A probability distribution for values of the effective climate sensitivity, with a lower bound of 1.6 K (5th percentile), is obtained on the basis of the increase in ocean heat content in recent decades from analyses of observed interior-ocean temperature changes, surface temperature changes measured since 1860, and estimates of anthropogenic and natural radiative forcing of the climate system. Radiative forcing is the greatest source of uncertainty in the calculation; the result also depends somewhat on the rate of ocean heat uptake in the late nineteenth century, for which an assumption is needed as there is no observational estimate. Because the method does not use the climate sensitivity simulated by a general circulation model, it provides an independent observationally based constraint on this important parameter of the climate system.

The Global Ocean Data Assimilation Experiment High-resolution Sea Surface Temperature Pilot Project

A new generation of integrated sea surface temperature (SST) data products are being provided by the Global Ocean Data Assimilation Experiment (GODAE) High-Resolution SST Pilot Project (GHRSST-PP). These combine in near-real time various SST data products from several different satellite sensors and in situ observations and maintain the fine spatial and temporal resolution needed by SST inputs to operational models. The practical realization of such an approach is complicated by the characteristic differences that exist between measurements of SST obtained from subsurface in-water sensors, and satellite microwave and satellite infrared radiometer systems. Furthermore, diurnal variability of SST within a 24-h period, manifested as both warm-layer and cool-skin deviations, introduces additional uncertainty for direct intercomparison between data sources and the implementation of data-merging strategies. The GHRSST-PP has developed and now operates an internationally distributed system that provides operatio...

A new global gridded radiosonde temperature data base and recent temperature trends

We present a new analysis of global radiosonde temperature data. From 1979 onwards, the data from the Australasian region have been corrected for instrument-related discontinuities with the help of comparisons with collocated retrievals from satellite-based Microwave Sounding Units (MSU) and metadata: in future work, adjustments will be applied worldwide and extended to earlier years. The data are stored as monthly anomalies from a 1971–1990 reference period on a 5° latitude × 10° longitude grid at 8 levels from 50 hPa to 850 hPa. Seasonal and annual temperature anomalies have also been created on a 10° × 20° grid using an eigenvector reconstruction method to filter noise. Latitude-height profiles of zonal-mean temperature changes since the 1960s show significant cooling in the lower stratosphere, especially in middle and high latitudes of the Southern Hemisphere, but the cooling over Australasia is less than shown by unadjusted data. Warming dominates the troposphere but is not a maximum in the tropical upper troposphere. In the annual mean, tropospheric warming is greatest around 45°N and possibly in the data-sparse high latitudes of the Southern Hemisphere.

Daily mean sea level pressure reconstructions for the European-North Atlantic region for the period 1850-2003

Abstract The development of a daily historical European–North Atlantic mean sea level pressure dataset (EMSLP) for 1850–2003 on a 5° latitude by longitude grid is described. This product was produced using 86 continental and island stations distributed over the region 25°–70°N, 70°W–50°E blended with marine data from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS). The EMSLP fields for 1850–80 are based purely on the land station data and ship observations. From 1881, the blended land and marine fields are combined with already available daily Northern Hemisphere fields. Complete coverage is obtained by employing reduced space optimal interpolation. Squared correlations (r 2) indicate that EMSLP generally captures 80%–90% of daily variability represented in an existing historical mean sea level pressure product and over 90% in modern 40-yr European Centre for Medium-Range Weather Forecasts Re-Analyses (ERA-40) over most of the region. A lack of sufficient observations over Greenland and...

The Met Office Hadley Centre sea ice and sea surface temperature data set, version 2: 1. Sea ice concentrations

We present a new version of the sea ice concentration component of the Met Office Hadley Centre sea ice and sea surface temperature data set, HadISST.2.1.0.0. Passive microwave data are combined with historical sources, such as sea ice charts, to create global analyses on a 1° grid from 1850 to 2007. Climatology was used when no information about the sea ice was available. Our main aim was to create a homogenous data set by calculating and applying bias adjustments using periods of overlaps between the different data sources used. National Ice Center charts from 1995 to 2007 have been used as a reference to achieve this. In particular, large bias adjustments have been applied to the passive microwave data in both the Antarctic and Arctic summers. Overall, HadISST.2.1.0.0 contains more ice than HadISST1.1, with higher concentrations, shorter marginal ice zones, and larger extents and areas in some regions and periods. A new method for estimating the concentrations within the ice pack using the distance from the ice edge has been developed and evaluated. This was used when only the extents were known or the original concentration fields were heterogeneous. A number of discontinuities in the HadISST1.1 record are no longer found in HadISST.2.1.0.0.