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  • Author or Editor: Aiguo Dai x
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Aiguo Dai
and
Kevin E. Trenberth

Abstract

Annual and monthly mean values of continental freshwater discharge into the oceans are estimated at 1° resolution using several methods. The most accurate estimate is based on streamflow data from the world's largest 921 rivers, supplemented with estimates of discharge from unmonitored areas based on the ratios of runoff and drainage area between the unmonitored and monitored regions. Simulations using a river transport model (RTM) forced by a runoff field were used to derive the river mouth outflow from the farthest downstream gauge records. Separate estimates are also made using RTM simulations forced by three different runoff fields: 1) based on observed streamflow and a water balance model, and from estimates of precipitation P minus evaporation E computed as residuals from the atmospheric moisture budget using atmospheric reanalyses from 2) the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) and 3) the European Centre for Medium-Range Weather Forecasts (ECMWF). Compared with previous estimates, improvements are made in extending observed discharge downstream to the river mouth, in accounting for the unmonitored streamflow, in discharging runoff at correct locations, and in providing an annual cycle of continental discharge. The use of river mouth outflow increases the global continental discharge by ∼19% compared with unadjusted streamflow from the farthest downstream stations. The river-based estimate of global continental discharge presented here is 37 288 ± 662 km3 yr−1, which is ∼7.6% of global P or 35% of terrestrial P. While this number is comparable to earlier estimates, its partitioning into individual oceans and its latitudinal distribution differ from earlier studies. The peak discharges into the Arctic, the Pacific, and global oceans occur in June, versus May for the Atlantic and August for the Indian Oceans. Snow accumulation and melt are shown to have large effects on the annual cycle of discharge into all ocean basins except for the Indian Ocean and the Mediterranean and Black Seas. The discharge and its latitudinal distribution implied by the observation-based runoff and the ECMWF reanalysis-based PE agree well with the river-based estimates, whereas the discharge implied by the NCEP–NCAR reanalysis-based PE has a negative bias.

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Aiguo Dai
,
Kevin E. Trenberth
, and
Taotao Qian

Abstract

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 2.5° 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 = 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 = 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 < −3.0, 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 > +3.0) declined slightly during the 1980s. Together, the global land areas in either very dry or very wet conditions have increased from ∼20% 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.

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Kevin E. Trenberth
,
Lesley Smith
,
Taotao Qian
,
Aiguo Dai
, and
John Fasullo

Abstract

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 EP 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.

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Taotao Qian
,
Aiguo Dai
,
Kevin E. Trenberth
, and
Keith W. Oleson

Abstract

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 (∼1.875°) 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.

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Alan K. Betts
,
John H. Ball
,
Pedro Viterbo
,
Aiguo Dai
, and
José Marengo

Abstract

The hydrometeorology of the Amazon basin in the ERA-40 reanalysis for 1958–2001 is compared with observations of precipitation, temperature, and streamflow. After 1979, the reanalysis over the Amazon has a small cool bias of the order of −0.35 K, and a small low bias of precipitation of the order of −0.3 mm day−1. In the early years (1958–72), there is a large upward drift in reanalysis precipitation and runoff associated with an upward drift in the atmospheric water vapor in the analysis, and a somewhat smaller downward drift of temperature as precipitation increases. In the presatellite data, there are inhomogeneities in the radiosonde and surface synoptic data, and there were problems with the variational analysis of humidity once satellite radiances were introduced. Approximate bias corrections can be made for precipitation and runoff on an annual basis, but this also removes some of the interannual variability. The reanalysis runoff–precipitation relationship is similar to the observed streamflow–precipitation relation, on an annual water-year basis. Compared to observations, ERA-40 precipitation for the Amazon is low by about 1.3 mm day−1 in the rainy season, and high by a smaller amount in the dry season. The precipitation bias produces a temperature bias in ERA-40 of the opposite sign on the annual time scale. The reanalysis has a small cold temperature bias after 1967, but on an annual time scale it reproduces the interannual variability of the observations. Although the biases in temperature and precipitation in recent decades are small, the difficulties with the analysis of atmospheric water vapor lead to large uncertainty in long-term trends of the water cycle.

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Roy Rasmussen
,
Kyoko Ikeda
,
Changhai Liu
,
David Gochis
,
Martyn Clark
,
Aiguo Dai
,
Ethan Gutmann
,
Jimy Dudhia
,
Fei Chen
,
Mike Barlage
,
David Yates
, and
Guo Zhang

Abstract

A high-resolution climate model (4-km horizontal grid spacing) is used to examine the following question: How will long-term changes in climate impact the partitioning of annual precipitation between evapotranspiration and runoff in the Colorado Headwaters?

This question is examined using a climate sensitivity approach in which eight years of current climate is compared to a future climate created by modifying the current climate signal with perturbation from the NCAR Community Climate System Model, version 3 (CCSM3), model forced by the A1B scenario for greenhouse gases out to 2050. The current climate period is shown to agree well with Snowpack Telemetry (SNOTEL) surface observations of precipitation (P) and snowpack, as well as streamflow and AmeriFlux evapotranspiration (ET) observations. The results show that the annual evaporative fraction (ET/P) for the Colorado Headwaters is 0.81 for the current climate and 0.83 for the future climate, indicating increasing aridity in the future despite a positive increase of precipitation. Runoff decreased by an average of 6%, reflecting the increased aridity.

Precipitation increased in the future winter by 12%, but decreased in the summer as a result of increased low-level inhibition to convection. The fraction of precipitation that fell as snow decreased from 0.83 in the current climate to 0.74 in the future. Future snowpack did not change significantly until January. From January to March the snowpack increased above ~3000 m MSL and decreased below that level. Snowpack decreased at all elevations in the future from April to July. The peak snowpack and runoff over the headwaters occurred 2–3 weeks earlier in the future simulation, in agreement with previous studies.

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