[1] 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.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2002JD002670

Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century

Many scientific phenomena are now investigated by complex computer models or codes A computer experiment is a number of runs of the code with various inputs A feature of many computer experiments is that the output is deterministic--rerunning the code with the same inputs gives identical observations Often, the codes are computationally expensive to run, and a common objective of an experiment is to fit a cheaper predictor of the output to the data Our approach is to model the deterministic output as the realization of a stochastic process, thereby providing a statistical basis for designing experiments (choosing the inputs) for efficient prediction With this model, estimates of uncertainty of predictions are also available Recent work in this area is reviewed, a number of applications are discussed, and we demonstrate our methodology with an example

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The design and analysis of computer experiments

Statistics for Spatial Data.

Significantly more carbon is stored in the world's soils--including peatlands, wetlands and permafrost--than is present in the atmosphere. Disagreement exists, however, regarding the effects of climate change on global soil carbon stocks. If carbon stored belowground is transferred to the atmosphere by a warming-induced acceleration of its decomposition, a positive feedback to climate change would occur. Conversely, if increases of plant-derived carbon inputs to soils exceed increases in decomposition, the feedback would be negative. Despite much research, a consensus has not yet emerged on the temperature sensitivity of soil carbon decomposition. Unravelling the feedback effect is particularly difficult, because the diverse soil organic compounds exhibit a wide range of kinetic properties, which determine the intrinsic temperature sensitivity of their decomposition. Moreover, several environmental constraints obscure the intrinsic temperature sensitivity of substrate decomposition, causing lower observed 'apparent' temperature sensitivity, and these constraints may, themselves, be sensitive to climate.

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Temperature sensitivity of soil carbon decomposition and feedbacks to climate change

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...

An Improved In Situ and Satellite SST Analysis for Climate

A major challenge in predicting Earth's future climate state is to understand feedbacks that alter greenhouse-gas forcing. Here we synthesize field data from arctic Alaska, showing that terrestrial changes in summer albedo contribute substantially to recent high-latitude warming trends. Pronounced terrestrial summer warming in arctic Alaska correlates with a lengthening of the snow-free season that has increased atmospheric heating locally by about 3 watts per square meter per decade (similar in magnitude to the regional heating expected over multiple decades from a doubling of atmospheric CO2). The continuation of current trends in shrub and tree expansion could further amplify this atmospheric heating by two to seven times.

/pdf/role-of-land-surface-changes-in-arctic-summer-warming-6n2d2ylj4b.pdf

Role of Land-Surface Changes in Arctic Summer Warming

A gridded sea-ice database, for which the record length is now approaching four decades for the Arctic and two decades for the Antarctic, is summarized here. The sea-ice fluctuations derived from the dataset are characterized by temporal scales of several seasons to several years and spatial scales of 30-180 deg of longitude. The ice data are examined in conjunction with air temperature data for evidence of recent climate change in the polar regions. The arctic sea-ice variations over the past several decades are compatible with the corresponding air temperatures, which show a distinct warming that is strongest over northern land areas during the winter and spring. Statistically significant decreases of the summer extent of arctic ice are apparent in the sea-ice data, and new summer minima have been achieved three times in the past 15 years. There is no significant trend if ice extent in the Arctic during winter or in the Antarctic during any season.

Recent Variations of Sea Ice and Air Temperature in High Latitudes

Arctic sea level pressure data from the period of the Arctic Ocean Buoy Program show a significant decrease in the annual mean. In every calendar month, the annual mean is lower in the second half of the 1979–1994 period than in the first. The changes of the annual means are larger in the central Arctic than anywhere else in the Northern Hemisphere. The decreases are largest and statistically significant in the autumn and winter. The annual anomalies became negative relative to the 16-yr mean in the 1980s and have been negative in every year since 1988. Correspondingly, the mean anticyclone in the Arctic pressure field has weakened and the vorticity of the gradient wind field over the central Arctic Ocean has become more positive than at any time in the past several decades. The pressure decrease, which has been compensated by pressure increases over the subpolar oceans, implies that the wind forcing of sea ice contains an enhanced cyclonic component relative to earlier decades.

Recent decrease of sea level pressure in the central Arctic

Simulations of Arctic surface air temperature and sea level pressure by 14 global climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change are synthesized in an analysis of biases and trends. Simulated composite GCM surface air temperatures for 1981–2000 are generally 1°–2°C colder than corresponding observations with the exception of a cold bias maximum of 6°–8°C in the Barents Sea. The Barents Sea bias, most prominent in winter and spring, occurs in 12 of the 14 GCMs and corresponds to a region of oversimulated sea ice. All models project a twenty-first-century warming that is largest in the autumn and winter, although the rates of the projected warming vary considerably among the models. The across-model and across-scenario uncertainties in the projected temperatures are comparable through the first half of the twenty-first century, but increases in variability associated with the choice of scenario begin to outpace increases in across-model variabil...

Simulations of Arctic Temperature and Pressure by Global Coupled Models

Monthly surface air temperatures from land surface stations, automatic weather stations, and ship/buoy observations from the high-latitude Southern Hemisphere are synthesized into gridded analyses at a resolution appropriate for applications ranging from spatial trend analyses to climate change impact assessments. Correlation length scales are used to enhance information content while limiting the spatial extent of influence of the sparse data in the Antarctic region. The correlation length scales are generally largest in summer and over the Antarctic continent, while they are shortest over the winter sea ice. Gridded analyses of temperature anomalies, limited to regions within a correlation length scale of at least one observation, are constructed and validated against observed temperature anomalies in single-station-out experiments. Trends calculated for the 1958–2002 period suggest modest warming over much of the 60°–90°S domain. All seasons show warming, with winter trends being the largest a...

William L. Chapman

Papers

Role of Land-Surface Changes in Arctic Summer Warming

Recent Variations of Sea Ice and Air Temperature in High Latitudes

Recent decrease of sea level pressure in the central Arctic

Simulations of Arctic Temperature and Pressure by Global Coupled Models

A Synthesis of Antarctic Temperatures