A monthly dataset of Palmer Drought Severity Index (PDSI) from 1870 to 2002 is derived using historical precipitation and temperature data for global land areas on a 2.5Њ grid. Over Illinois, Mongolia, and parts of China and the former Soviet Union, where soil moisture data are available, the PDSI is significantly correlated (r ϭ 0.5 to 0.7) with observed soil moisture content within the top 1-m depth during warm-season months. The strongest correlation is in late summer and autumn, and the weakest correlation is in spring, when snowmelt plays an important role. Basin-averaged annual PDSI covary closely (r ϭ 0.6 to 0.8) with streamflow for seven of world's largest rivers and several smaller rivers examined. The results suggest that the PDSI is a good proxy of both surface moisture conditions and streamflow. An empirical orthogonal function (EOF) analysis of the PDSI reveals a fairly linear trend resulting from trends in precipitation and surface temperature and an El Niño-Southern Oscillation (ENSO)-induced mode of mostly interannual variations as the two leading patterns. The global very dry areas, defined as PDSI Ͻ Ϫ3.0, have more than doubled since the 1970s, with a large jump in the early 1980s due to an ENSO-induced precipitation decrease and a subsequent expansion primarily due to surface warming, while global very wet areas (PDSI Ͼ ϩ3.0) declined slightly during the 1980s. Together, the global land areas in either very dry or very wet conditions have increased from ϳ20% to 38% since 1972, with surface warming as the primary cause after the mid-1980s. These results provide observational evidence for the increasing risk of droughts as anthropogenic global warming progresses and produces both increased temperatures and increased drying.

A new dataset of historical monthly streamflow at the farthest downstream stations for the world's 925 largest ocean-reaching rivers has been created for community use. Available new gauge records are added to a network of gauges that covers ;80 3 10 6 km 2 or ;80% of global ocean-draining land areas and accounts for about 73% of global total runoff. For most of the large rivers, the record for 1948-2004 is fairly complete. Data gaps in the records are filled through linear regression using streamflow simulated by a land surface model [Community Land Model, version 3 (CLM3)] forced with observed precipitation and other atmospheric forcings that are significantly (and often strongly) correlated with the observed streamflow for most rivers. Compared with previous studies, the new dataset has improved homogeneity and enables more reliable assessments of decadal and long-term changes in continental freshwater discharge into the oceans. The model-simulated runoff ratio over drainage areas with and without gauge records is used to estimate the contribution from the areas not monitored by the gauges in deriving the total discharge into the global oceans.Results reveal large variations in yearly streamflow for most of the world's large rivers and for continental discharge, but only about one-third of the top 200 rivers (including the Congo, Mississippi, Yenisey, Paraná, Ganges, Columbia, Uruguay, and Niger) show statistically significant trends during 1948-2004, with the rivers having downward trends (45) outnumbering those with upward trends (19). The interannual variations are correlated with the El Niñ o-Southern Oscillation (ENSO) events for discharge into the Atlantic, Pacific, Indian, and global ocean as a whole. For ocean basins other than the Arctic, and for the global ocean as a whole, the discharge data show small or downward trends, which are statistically significant for the Pacific (29.4 km 3 yr 21 ). Precipitation is a major driver for the discharge trends and large interannual-to-decadal variations. Comparisons with the CLM3 simulation suggest that direct human influence on annual streamflow is likely small compared with climatic forcing during 1948-2004 for most of the world's major rivers. For the Arctic drainage areas, upward trends in streamflow are not accompanied by increasing precipitation, especially over Siberia, based on available data, although recent surface warming and associated downward trends in snow cover and soil ice content over the northern high latitudes contribute to increased runoff in these regions. The results are qualitatively consistent with climate model projections but contradict an earlier report of increasing continental runoff during the recent decades based on limited records.

[1] The Community Land Model version 3 (CLM3) is the land component of the Community Climate System Model (CCSM). CLM3 has energy and water biases resulting from deficiencies in some of its canopy and soil parameterizations related to hydrological processes. Recent research by the community that utilizes CLM3 and the family of CCSM models has indicated several promising approaches to alleviating these biases. This paper describes the implementation of a selected set of these parameterizations and their effects on the simulated hydrological cycle. The modifications consist of surface data sets based on Moderate Resolution Imaging Spectroradiometer products, new parameterizations for canopy integration, canopy interception, frozen soil, soil water availability, and soil evaporation, a TOPMODEL-based model for surface and subsurface runoff, a groundwater model for determining water table depth, and the introduction of a factor to simulate nitrogen limitation on plant productivity. The results from a set of offline simulations were compared with observed data for runoff, river discharge, soil moisture, and total water storage to assess the performance of the new model (referred to as CLM3.5). CLM3.5 exhibits significant improvements in its partitioning of global evapotranspiration (ET) which result in wetter soils, less plant water stress, increased transpiration and photosynthesis, and an improved annual cycle of total water storage. Phase and amplitude of the runoff annual cycle is generally improved. Dramatic improvements in vegetation biogeography result when CLM3.5 is coupled to a dynamic global vegetation model. Lower than observed soil moisture variability in the rooting zone is noted as a remaining deficiency.

Because of a lack of observations, historical simulations of land surface conditions using land surface models are needed for studying variability and changes in the continental water cycle and for providing initial conditions for seasonal climate predictions. Atmospheric forcing datasets are also needed for land surface model development. The quality of atmospheric forcing data greatly affects the ability of land surface models to realistically simulate land surface conditions. Here a carefully constructed global forcing dataset for 1948-2004 with 3-hourly and T62 (ϳ1.875°) resolution is described, and historical simulations using the latest version of the Community Land Model version 3.0 (CLM3) are evaluated using available observations of streamflow, continental freshwater discharge, surface runoff, and soil moisture. The forcing dataset was derived by combining observation-based analyses of monthly precipitation and surface air temperature with intramonthly variations from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis, which is shown to have spurious trends and biases in surface temperature and precipitation. Surface downward solar radiation from the reanalysis was first adjusted for variations and trends using monthly station records of cloud cover anomaly and then for mean biases using satellite observations during recent decades. Surface specific humidity from the reanalysis was adjusted using the adjusted surface air temperature and reanalysis relative humidity. Surface wind speed and air pressure were interpolated directly from the 6-hourly reanalysis data. Sensitivity experiments show that the precipitation adjustment (to the reanalysis data) leads to the largest improvement, while the temperature and radiation adjustments have only small effects.When forced by this dataset, the CLM3 reproduces many aspects of the long-term mean, annual cycle, interannual and decadal variations, and trends of streamflow for many large rivers (e.g., the Orinoco, Changjiang, Mississippi, etc.), although substantial biases exist. The simulated long-term-mean freshwater discharge into the global and individual oceans is comparable to 921 river-based observational estimates. Observed soil moisture variations over Illinois and parts of Eurasia are generally simulated well, with the dominant influence coming from precipitation. The results suggest that the CLM3 simulations are useful for climate change analysis. It is also shown that unrealistically low intensity and high frequency of precipitation, as in most model-simulated precipitation or observed time-averaged fields, result in too much evaporation and too little runoff, which leads to lower than observed river flows. This problem can be reduced by adjusting the precipitation rates using observed-precipitation frequency maps.

A brief review is given of research in the Climate Analysis Section at NCAR on the water cycle. Results are used to provide a new estimate of the global hydrological cycle for long-term annual means that includes estimates of the main reservoirs of water as well as the flows of water among them. For precipitation P over land a comparison among three datasets enables uncertainties to be estimated. In addition, results are presented for the mean annual cycle of the atmospheric hydrological cycle based on 1979–2000 data. These include monthly estimates of P, evapotranspiration E, atmospheric moisture convergence over land, and changes in atmospheric storage, for the major continental landmasses, zonal means over land, hemispheric land means, and global land means. The evapotranspiration is computed from the Community Land Model run with realistic atmospheric forcings, including precipitation that is constrained by observations for monthly means but with high-frequency information taken from atmospheric reanalyses. Results for E − P are contrasted with those from atmospheric moisture budgets based on 40-yr ECMWF Re-Analysis (ERA-40) data. The latter show physically unrealistic results, because evaporation often exceeds precipitation over land, especially in the Tropics and subtropics.

The trends of the surface water and energy budget components in the Mississippi River basin from 1948 to 2004 are investigated using a combination of hydrometeorological observations and observation-constrained simulations of the land surface conditions using the latest version of the Community Land Model version 3 (CLM3). The atmospheric forcing data for the CLM3 were constructed by adding the intramonthly variations from the 6-hourly National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis to observation-based analyses of monthly precipitation, surface air temperature, and cloud cover. The model-based analysis suggests that, for the surface water budget, the observed increase in basin-averaged precipitation is compensated by increases in both runoff and evapotranspiration. For the surface energy budget, the decrease of net shortwave radiation associated with observed increases in cloudiness is compensated by decreases in both net longwave radiation and sensible heat flux, while the latent heat flux increases in association with wetter soil conditions. Both the simulated surface water and energy budgets support the view that evapotranspiration has increased in the Mississippi River basin from 1948 to 2004. Sensitivity experiments show that the precipitation change dominates the evapotranspiration trend, while the temperature and solar radiation changes have only small effects. Large spatial variations within the Mississippi River basin and the contiguous United States are also found. However, the increased evapotranspiration is ubiquitous despite spatial variations in hydrometeorology.

This study examines the stratocumulus clouds and associated cloud feedback in the southeast Pacific (SEP) simulated by eight global climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and Cloud Feedback Model Intercomparison Project (CFMIP) using long-term observations of clouds, radiative fluxes, cloud radiative forcing (CRF), sea surface temperature (SST), and large-scale atmosphere environment. The results show that the state-of-the-art global climate models still have significant difficulty in simulating the SEP stratocumulus clouds and associated cloud feedback. Comparing with observations, the models tend to simulate significantly less cloud cover, higher cloud top, and a variety of unrealistic cloud albedo. The insufficient cloud cover leads to overly weak shortwave CRF and net CRF. Only two of the eight models capture the observed positive cloud feedback at subannual to decadal time scales. The cloud and radiation biases in the models are associated with 1) model biases in large-scale temperature structure including the lack of temperature inversion, insufficient lower troposphere stability (LTS), and insufficient reduction of LTS with local SST warming, and 2) improper model physics, especially insufficient increase of low cloud cover associated with larger LTS. The two models that arguably do best at simulating the stratocumulus clouds and associated cloud feedback are the only ones using cloud-top radiative cooling to drive boundary layer turbulence.

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