[1] Recent developments in observational near-surface air temperature and sea-surface temperature analyses are combined to produce HadCRUT4, a new data set of global and regional temperature evolution from 1850 to the present. This includes the addition of newly digitized measurement data, both over land and sea, new sea-surface temperature bias adjustments and a more comprehensive error model for describing uncertainties in sea-surface temperature measurements. An ensemble approach has been adopted to better describe complex temporal and spatial interdependencies of measurement and bias uncertainties and to allow these correlated uncertainties to be taken into account in studies that are based upon HadCRUT4. Climate diagnostics computed from the gridded data set broadly agree with those of other global near-surface temperature analyses. Fitted linear trends in temperature anomalies are approximately 0.07°C/decade from 1901 to 2010 and 0.17°C/decade from 1979 to 2010 globally. Northern/southern hemispheric trends are 0.08/0.07°C/decade over 1901 to 2010 and 0.24/0.10°C/decade over 1979 to 2010. Linear trends in other prominent near-surface temperature analyses agree well with the range of trends computed from the HadCRUT4 ensemble members.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2011JD017187

Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set

Downscaling of climate model data is essential to local and regional impact analysis. We compare two meth- ods of statistical downscaling to produce continuous, grid- ded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km 2 per grid cell) reso- lution over the western U.S. We use NCEP/NCAR Reanaly- sis data from 1950-1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscal- ing (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA down- scales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to gener- ate daily values. The methods produce generally comparable skill in producing downscaled, gridded fields of precipita- tion and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit limited skill in re- producing both observed wet and dry extremes and the dif- ference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the re- analysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demon- strates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day cor- respondence of weather from the GCM needs to be repro-

Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods

/pdf/utility-of-daily-vs-monthly-large-scale-climate-data-an-5cc7e6bkb4.pdf

An automated homogenization algorithm based on the pairwise comparison of monthly temperature series is described. The algorithm works by forming pairwise difference series between serial monthly temperature values from a network of observing stations. Each difference series is then evaluated for undocumented shifts, and the station series responsible for such breaks is identified automatically. The algorithm also makes use of station history information, when available, to improve the identification of artificial shifts in temperature data. In addition, an evaluation is carried out to distinguish trend inhomogeneities from abrupt shifts. When the magnitude of an apparent shift attributed to a particular station can be reliably estimated, an adjustment is made for the target series. The pairwise algorithm is shown to be robust and efficient at detecting undocumented step changes under a variety of simulated scenarios with step- and trend-type inhomogeneities. Moreover, the approach is shown to yield a lower false-alarm rate for undocumented changepoint detection relative to the more common use of a reference series. Results from the algorithm are used to assess evidence for trend inhomogeneities in U.S. monthly temperature data.

Homogenization of Temperature Series via Pairwise Comparisons

[1] Most global temperature analyses are based on station air temperatures. This study presents a global analysis of the relationship between remotely sensed annual maximum LST (LSTmax) from the Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and the corresponding site-based maximum air temperature (Tamax) for every World Meteorological Organization station on Earth. The relationship is analyzed for different land cover types. We observed a strong positive correlation between LSTmax and Tamax. As temperature increases, LSTmax increases faster than Tamax and captures additional information on the concentration of thermal energy at the Earth's surface, and biophysical controls on surface temperature, such as surface roughness and transpirational cooling. For hot conditions and in nonforested cover types, LST is more closely coupled to the radiative and thermodynamic characteristics of the Earth than the air temperature (Tair). Barren areas, shrublands, grasslands, savannas, and croplands have LSTmax values between 10°C and 20°C hotter than the corresponding Tamax at higher temperatures. Forest cover types are the exception with a near 1:1 relationship between LSTmax and Tamax across the temperature range and 38°C as the approximate upper limit of LSTmax with the exception of subtropical deciduous forest types where LSTmax occurs after canopy senescence. The study shows a complex interaction between land cover and surface energy balances. This global, semiautomated annual analysis could provide a new, unique, monitoring metric for integrating land cover change and energy balance changes.

/pdf/a-global-comparison-between-station-air-temperatures-and-tdmcwj837a.pdf

A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests

[1] We use the “observation minus reanalysis” difference (OMR) method to estimate the impact of land-use changes by computing the difference between the trends of the surface temperature observations (which reflect all the sources of climate forcing, including surface effects) and the NCEP-NCAR reanalysis surface temperatures (only influenced by the assimilated atmospheric temperature trends). This includes not only urbanization effects but also changes in agricultural practices, such as irrigation and deforestation, as well as other near-surface forcings related to industrialization, such as aerosols. We slightly correct previous results by including the year 1979 within the satellite decades and by excluding stations in the West Coast of the United States. The OMR estimate for surface impact on the mean temperature is similar to that obtained using satellite observations of night light to discriminate between rural and urban stations, with regions of large positive and negative trends, in contrast with the urban corrections based on population density, which are uniformly positive and much smaller. The OMR seasonal cycle results suggest that the impact of the greenhouse gases dominates in the winter, whereas it appears that the impact of surface forcings dominates in the summer. The impact of the USHCN adjustments for nonclimatic trends in the observations does not affect the geographical distribution of the OMR trends. The effect of using a model with constant CO2 in the reanalysis, the use of other reanalyses, and the possible use of the reanalyses to correct for nonclimatic jumps in the observations are also discussed.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2005JD006555

Jayakar Tobin

Papers

Estimation of the impact of land-surface forcings on temperature trends in eastern United States