Abstract. Global drylands encompassing hyper-arid, arid, semiarid, and dry subhumid areas cover about 41 percent of the earth's terrestrial surface and are home to more than a third of the world's population. By analyzing observations for 1948-2008 and climate model simulations for 1948-2100, we show that global drylands have expanded in the last sixty years and will continue to expand in the 21st century. By the end of this century, the world's drylands (under a high greenhouse gas emission scenario) are projected to be 5.8 × 10 6 km 2 (or 10 %) larger than in the 1961-1990 climatology. The major expansion of arid regions will occur over southwest North America, the northern fringe of Africa, southern Africa, and Australia, while major expansions of semiarid regions will occur over the north side of the Mediterranean, southern Africa, and North and South America. The global dryland expansions will increase the population affected by water scarcity and land degradations.

The dryness of terrestrial climate can be measured by the ratio of annual precipitation (P) to potential evapotranspiration (PET), where the latter represents the evaporative demand of the atmosphere, which depends on the surface air temperature, relative humidity, wind speed, and available energy. This study examines how the terrestrial mean aridity responds to global warming in terms of P/PET using the Coupled Model Intercomparison Project phase 5 transient CO 2 increase to 2 × CO 2 simulations. We show that the (percentage) increase (rate) in P averaged over land is~1.7%/°C ocean mean surface air temperature increase, while the increase in PET is 5.3%/°C, leading to a decrease in P/PET (i.e., a drier terrestrial climate) by~3.4%/°C. Noting a similar rate of percentage increase in P over land to that in evaporation (E) over ocean, we propose a framework for examining the change in P/PET, in which we compare the change in PET over land and E over ocean, both expressed using the Penman-Monteith formula. We show that a drier terrestrial climate is caused by (i) enhanced land warming relative to the ocean, (ii) a decrease in relative humidity over land but an increase over ocean, (iii) part of increase in net downward surface radiation going into the deep ocean, and (iv) different responses of PET over land and E over ocean for given changes in atmospheric conditions (largely associated with changes in temperatures). The relative contributions to the change in terrestrial mean aridity from these four factors are about 35%, 35%, 15%, and 15%, respectively. The slight slowdown of the surface wind over both land and ocean has little impact on the terrestrial mean aridity.

Long-term observational data are essential for understanding local and regional climate and climate change. These data are also important for hydrological designs and agricultural decision making. This study examined the daily meteorological data from 726 stations in China from 1951 to 2000, and developed an unprecedented climatic dataset that contains 10 daily variables: maximum and minimum surface air temperatures, mean surface air temperature, skin surface temperature, surface air relative humidity, wind speed, wind gust, sunshine duration hours, precipitation, and pan evaporation. The characteristics of the original stations' data and quality-control methods designed and used in developing this dataset are detailed. The quality-control procedures identified less than 0.05% of the data records as being erroneous because of typos and incorrect units, reading, or data coding. When the spatial and temporal consistency of the variables' time series were inspected, nearly 37.9% of the stations were found to have one or more variables with inconsistent changes. The sources causing the temporal inconsistency/discontinuity were evaluated, and a method was developed and applied to adjust those data segments containing inconsistent values. The resulting data series, as an alternative to the original quality-controlled series, showed both spatially and temporally consistent trends in the occurrence frequency of extreme climate events compared with the unadjusted data series. Finally, the quality-controlled daily data were gridded to a 1.0°×1.0°grid system covering China after the erroneous and missing data were estimated. This new dataset opens up opportunities for analysing and understanding the climate variability and climate change in China.

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