Assessing a Satellite-Era Perspective of the Global Water Cycle

2007 ◽  
Vol 20 (7) ◽  
pp. 1316-1338 ◽  
Author(s):  
C. Adam Schlosser ◽  
Paul R. Houser

Abstract The capability of a global data compilation, largely satellite based, is assessed to depict the global atmospheric water cycle’s mean state and variability. Monthly global precipitation estimates from the Global Precipitation Climatology Project (GPCP) and the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) span from 1979 to 1999. Monthly global Special Sensor Microwave Imager (SSM/I)-based bulk aerodynamic ocean evaporation estimates span from June 1987 to December 1999. Global terrestrial evapotranspiration rates are estimated over a multidecade period (1975–99) using a global land model simulation forced by bias-corrected reanalysis data. Monthly total precipitable water (TPW) from the NASA Global Water Vapor Project (NVAP) spans from 1988 to 1999. The averaged annual global precipitation (P) and evaporation (E) estimates are out of balance by 5% or 24 000 (metric) gigatons (Gton) of water, which exceeds the uncertainty of global mean annual precipitation (∼±1%). For any given year, the annual flux imbalance can be on the order of 10% (48 000 Gton of water). However, observed global TPW interannual variations suggest a water flux imbalance on the order of 0.01% (48 Gton of water)—a finding consistent with a general circulation model (GCM) simulation. Variations in observationally based global P and E rates show weak monthly and interannual consistency, and depending on the choice of ocean evaporation data, the mean annual cycle of global E − P can be up to 5 times larger to that of TPW. The global ocean annual evaporation rates have as much as a ∼1% yr−1 increase during the period analyzed (1988–99), which is consistent in sign with most transient CO2 GCM simulations, but at least an order of magnitude larger. The ocean evaporation trends are driven by trends in SSM/I-retrieved near-surface atmospheric humidity and wind speed, and the largest year-to-year changes are coincident with transitions in the SSM/I fleet. In light of (potential) global water cycle changes in GCM projections, the ability to consistently detect or verify these changes in nature rests upon one or more of the following: quantification of global evaporation uncertainty, at least a twofold improvement in consistency between the observationally based global precipitation and evaporation variations, a two order of magnitude rectification between annual variations of E − P and precipitable water as well as substantial improvements in the consistency of their seasonal cycles, a critical reevaluation of intersatellite calibration for the relevant geophysical quantities used for ocean evaporation estimates, and the continuation of a dedicated calibration in this regard for future satellite transitions.


2020 ◽  
Author(s):  
Mijael Rodrigo Vargas Godoy ◽  
Rajani Kumar Pradhan ◽  
Shailendra Pratap ◽  
Akif Rahim ◽  
Yannis Markonis

<p>The knowledge of global precipitation is of crucial importance to the study of climate dynamics and the global water cycle in general. Although global precipitation climatologies have existed for some time, and their understanding has improved dramatically due to the vast amount of different data sources, their information has not been comprehensive enough due to precipitation spatial-temporal variability. Thus, ground station reports are, in some cases, not representative of the surrounding areas. Remote sensing data and model simulations complemented the traditional surface measurements and offered unprecedented coverage on a global scale. It is important to note that satellite data records are now of sufficient time frame lengths and with methods “mature” enough to develop meaningful precipitation climatologies that are able to provide information on precipitation patterns and intensities on a global scale. While data (and in some cases exploration/visualization tools as well) are widely available, each dataset comes with different spatial resolution, temporal resolution, and biases.</p><p>Consequently, this unique opportunity to obtain a robust quantification of global precipitation has been hindered by the uncertainty, already revealed in the first attempts of the unification of different data products. Herein, we present a multi-source quantification of global precipitation, focusing on the description of the underlying uncertainties. Our approach combines station (CRU, GHCN-M, PRECL, UDEL, and CPC Global), remote sensing (PERSIANN, PERSIANN-CCS, PERSIANN-CDR, GPCP, GPCP_PEN_v2.2, CMAP, and CPC-Global) and reanalysis (NCEP1, NCEP2, and 20CRv2) data products, providing an updated overview of the role of precipitation in global water cycle.</p>



1989 ◽  
Vol 289 (4) ◽  
pp. 455-483 ◽  
Author(s):  
Y. Tardy ◽  
R. N'Kounkou ◽  
J.-L. Probst


2007 ◽  
Vol 88 (3) ◽  
pp. 375-384 ◽  
Author(s):  
E. S. Takle ◽  
J. Roads ◽  
B. Rockel ◽  
W. J. Gutowski ◽  
R. W. Arritt ◽  
...  

A new approach, called transferability intercomparisons, is described for advancing both understanding and modeling of the global water cycle and energy budget. Under this approach, individual regional climate models perform simulations with all modeling parameters and parameterizations held constant over a specific period on several prescribed domains representing different climatic regions. The transferability framework goes beyond previous regional climate model intercomparisons to provide a global method for testing and improving model parameterizations by constraining the simulations within analyzed boundaries for several domains. Transferability intercomparisons expose the limits of our current regional modeling capacity by examining model accuracy on a wide range of climate conditions and realizations. Intercomparison of these individual model experiments provides a means for evaluating strengths and weaknesses of models outside their “home domains” (domain of development and testing). Reference sites that are conducting coordinated measurements under the continental-scale experiments under the Global Energy and Water Cycle Experiment (GEWEX) Hydrometeorology Panel provide data for evaluation of model abilities to simulate specific features of the water and energy cycles. A systematic intercomparison across models and domains more clearly exposes collective biases in the modeling process. By isolating particular regions and processes, regional model transferability intercomparisons can more effectively explore the spatial and temporal heterogeneity of predictability. A general improvement of model ability to simulate diverse climates will provide more confidence that models used for future climate scenarios might be able to simulate conditions on a particular domain that are beyond the range of previously observed climates.



Science ◽  
2012 ◽  
Vol 336 (6080) ◽  
pp. 455-458 ◽  
Author(s):  
P. J. Durack ◽  
S. E. Wijffels ◽  
R. J. Matear


1990 ◽  
Vol 66 (3-4) ◽  
pp. 303


2001 ◽  
Vol 32 (1-2) ◽  
pp. 231-246 ◽  
Author(s):  
Siegfried Franck ◽  
Christine Bounama


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