Paul A. Dirmeyer Center for Ocean-Land-Atmosphere Studies, Calverton, Maryland, USA Kaye L. Brubaker Dept. Civil and Environmental Eng., University of Maryland, College Park, Maryland, USA References: Bosilovich, M. G., and S. D. Schubert, 2001: Precipitation recycling over the central United States diagnosed from the GEOS-1 data assimilation system. J. Hydrometeor., 2, Brubaker, K. L., D. Entekhabi, and P. S. Eagleson, 1993: Estimation of continental precipitation recycling. J. Climate, 6, Brubaker, K. L., P. A. Dirmeyer, A. Sudradjat, B. S. Levy, and F. Bernal, 2001: A 36-year climatology of the evaporative sources of warm- season precipitation in the Mississippi river basin. J. Hydrometeor., 2, Budyko, M. I.,, 1974:. Climate and Life, Academic Press, New York, 508 pp. Burde, G. I., A. Zangvil, and P. J. Lamb, 1996: Estimating the role of local evaporation in precipitation for a two-dimensional region. J. Climate, 9, Dirmeyer, P. A., and K. L. Brubaker, 1999: Contrasting evaporative moisture sources during the drought of 1988 and the flood of J. Geophys. Res., 104, Druyan, L. M., and R. D. Koster, 1989: Sources of Sahel precipitation for simulated drought and rainy seasons. J. Climate, 2, Eltahir, E. A. B., and R. L. Bras, 1994: Precipitation recycling in the Amazon Basin. Quart. J. Roy. Meteor. Soc., 120, Henderson-Sellers, A., K. McGuffie, and H. Zhang, 2002: Stable isotopes as validation tools for global climate model predictions of the impact of Amazonian deforestation. J. Climate, 15, Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP-DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, Numaguti, A., 1999: Origin and recycling processes of precipitating water over the Eurasian continent: Experiments using an atmospheric general circulation model. J. Geophys. Res., 104, Sudradjat, A., 2002: Source-sink analysis of precipitation supply to large river basins. PhD Dissertation, [Available from University of Maryland, College Park, MD 20742, U.S.A.], 186 pp.. Trenberth, K. E., 1999: Atmospheric moisture recycling: Role of advection and local evaporation. J. Climate, 12, Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, Acknowledgements: This work was sponsored by National Science Foundation grants EAR (Dirmeyer) and EAR (Brubaker). References: Bosilovich, M. G., and S. D. Schubert, 2001: Precipitation recycling over the central United States diagnosed from the GEOS-1 data assimilation system. J. Hydrometeor., 2, Brubaker, K. L., D. Entekhabi, and P. S. Eagleson, 1993: Estimation of continental precipitation recycling. J. Climate, 6, Brubaker, K. L., P. A. Dirmeyer, A. Sudradjat, B. S. Levy, and F. Bernal, 2001: A 36-year climatology of the evaporative sources of warm- season precipitation in the Mississippi river basin. J. Hydrometeor., 2, Budyko, M. I.,, 1974:. Climate and Life, Academic Press, New York, 508 pp. Burde, G. I., A. Zangvil, and P. J. Lamb, 1996: Estimating the role of local evaporation in precipitation for a two-dimensional region. J. Climate, 9, Dirmeyer, P. A., and K. L. Brubaker, 1999: Contrasting evaporative moisture sources during the drought of 1988 and the flood of J. Geophys. Res., 104, Druyan, L. M., and R. D. Koster, 1989: Sources of Sahel precipitation for simulated drought and rainy seasons. J. Climate, 2, Eltahir, E. A. B., and R. L. Bras, 1994: Precipitation recycling in the Amazon Basin. Quart. J. Roy. Meteor. Soc., 120, Henderson-Sellers, A., K. McGuffie, and H. Zhang, 2002: Stable isotopes as validation tools for global climate model predictions of the impact of Amazonian deforestation. J. Climate, 15, Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP-DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, Numaguti, A., 1999: Origin and recycling processes of precipitating water over the Eurasian continent: Experiments using an atmospheric general circulation model. J. Geophys. Res., 104, Sudradjat, A., 2002: Source-sink analysis of precipitation supply to large river basins. PhD Dissertation, [Available from University of Maryland, College Park, MD 20742, U.S.A.], 186 pp.. Trenberth, K. E., 1999: Atmospheric moisture recycling: Role of advection and local evaporation. J. Climate, 12, Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, Acknowledgements: This work was sponsored by National Science Foundation grants EAR (Dirmeyer) and EAR (Brubaker). Motivation All fresh water on or beneath the land surface arrived as precipitation, and ultimately all of that water was evaporated from the oceans. However, it may have taken multiple “cycles” of precipitation and evaporation for any single water molecule to work its way from the ocean to a given terrestrial location, with evaporation from the land surface or transpiration through the terrestrial biosphere occurring in the intermediate cycles. Unlike over oceans, evapotranspiration over land is usually limited to a rate less than the potential rate due to stresses such as low soil moisture or sub-optimal conditions for photosynthesis in plants. Therefore, changing land surface conditions, whether caused directly by land use polices or as a response to fluctuations or trends in climate, can impact the hydrologic circuit between land and atmosphere by changing evapotranspiration rates. To understand future impacts, we must first understand the current climate. 6/2005 GEWEX Basins Basins show a variety of changes in recycling for extreme wet and dry years. Arid regions often show increased recycling during wet years because local evaporation increases dramatically. Some semi-arid and mid-latitude basins show decreased recycling in both wet (increased moisture advection and convergence) and dry (decreased local evaporation) years. The La Plata and Yangtze basins have highest recycling during dry years. Changes in source regions between wettest and driest years are rarely mirror images of each other. Often wet years are characterized by increased advection of moisture from over warm oceans or more humid latitudes. Droughts, on the other hand appear to have multiple causes. Scaling Because of its definition, the recycling ratio  is proportional to the area A under consideration. This property can make it difficult to compare the recycling between different regions. Fortunately, there is a strong log-log relationship between recycling ratio and area ( Sudradjat 2002): The Table lists 14 regions and the values of the recycling ratio calculated based on this regression calculated using areas of 64, 32, 16…1grid box(es). The COV of b is an order of magnitude smaller than for a and 14 times smaller than the mean. Thus, we can approximate a universal slope factor b to compare different regions. The value of the intercept a can be estimated from the regression relationship for any location. A global value of for b is used to scale the recycling ratios to a common area for plotting and comparison. Input Data Sets Near-surface meteorological data: National Centers for Environmental Prediction (NCEP) / Department of Energy (DOE) reanalysis (Kanamitsu et al. 2002). These data are used to calculate precipitable water, potential temperature, and the advection of water vapor. Sigma-level; global 192x94 grid (1.875° longitude by approximately 1.9° latitude);6 hourly; 1979-present. Use humidity, temperature, and wind (u and v components) on the 16 lowest model sigma levels; surface pressure, precipitation and total evaporation. To avoid spurious excess convergence toward the poles, the meridional wind is scaled by the cosine of latitude. The land-sea mask from the reanalysis is used to differentiate land grid boxes for the calculation. Global Characterization of Recycling and Evaporative Moisture Sources from a Quasi-Isentropic Back-Trajectory Analysis of Atmospheric Water Vapor Regression of recycling ratio versus area (example; inset) shows regions in all climates have a similar slope (right). This gives us a means to directly compare recycling in different regions using a common reference area. The 25-year seasonal mean recycling ratio for a 10 5 km 2 reference area (left); interannual coefficient of variation (center); 25-year trend (%/year) in recycling ratio (right). Conclusions QIBT method for estimating evaporative moisture sources and recycling has been extended globally for a 25+ year period. Using the latest data sets and minor improvements to the algorithms of Dirmeyer and Brubaker (1999) and Brubaker et al. (2001). Universal scaling is found for recycling ratio as a function of area, allowing for on par comparisons of recycling between different areas. The global mean recycling ratio for a reference area of 10 5 km 2 is about 4.5%. Mean seasonal recycling ratios show that minima are observed in regions with strong advection from adjacent waters. Recycling is higher over much of South America south of the Amazon River through the La Plata Basin, much of subtropical southern Africa, southern Europe (including the regions surrounding the Black Sea), and a broad swath of the high latitudes of the Northern Hemisphere during summer. Regions of high elevation such as western North America, the Bolivian Altiplano and Tibet, show very high recycling, due to the effect of very low precipitable water values on the calculations in the QIBT method. Comparison to estimates using a bulk method show similar overall patterns, although there are differences in magnitude and detail. For compatible spatial scales, the QIBT method results in higher recycling estimates. A notable exception is northern South America. Arid regions stand out as having a high degree of seasonal variation compared to the mean; the tropics have low COV. Interannual variability is greatest during the dry season in regions with strongly seasonal precipitation regimes. Broad high-latitude regions show positive trends during the boreal spring consistent with earlier onset of spring, There are also positive trends during SON over South Asia and southern Africa. There is a positive trend of 0.02% per year in the global mean annual recycling ratio. Climatology Seasonal mean conditions for the 25-year period are shown at right. High latitudes in summer, and high altitudes show highest recycling ratios. High latitudes in winter, deserts and the deep tropics have low values. Interannual variations are highest in dry regions. Spring trends over Canada and Alaska are consistent with an earlier onset of the growing season. Seasonal mean recycling ratios (%) computed using the bulk calculationof Trenberth (left – compare to QIBT estimates directly above); and normalized differences between QIBT and bulk methods (right) MethodExamplesCharacteristicsDrawbacks BulkBudyko (1974), Brubaker et al. (1993), Eltahir and Bras (1994), Burde et al. (1996) Uses time-mean data, vertically integrated. Storage change is insignificant. Atmosphere is well mixed. Averaging ignores nonlinearities in space (vertical) and time in advection equations. Calculate over pre- defined volumes. Trenberth (1999)Can be calculated locally, assuming a length scale. Well-mixed assumption may not be valid. IsotopicHenderson-Sellers et al. (2002) Measured ratio of water isotopes used to determine source (marine, terrestrial, transpiration). Cannot pinpoint location of evaporative source. Cannot be applied globally Tracer Modeling Druyan and Koster (1989), Numaguti (1999) Lagrangian tracers within a numerical atmospheric model Must choose source regions in advance. Computationally expensive. Skill depends on veracity of model climate. Bosilovich and Schubert (2001) Includes tracing through precipitation parameterizations. Comparison to Bulk Methods Trenberth (1999) computed recycling ratios on a global basis using a bulk formulation, where  is the recycling ratio, defined as the fraction of total precipitation ( P ) contributed by precipitation of local evaporative origin ( P m ), E is evapotranspiration, and F the average atmospheric moisture transport over the region. In the equation, L is an assigned length scale. For comparison to that study and to the QIBT recycling estimates obtained here, we have computed recycling using Trenberth’s equation with the data set assembled for this study. Trenberth (1999) obtained seasonal P from the Xie- Arkin product for , seasonal E from the NCEP reanalyses (6-hour model integrations), and F from the magnitude of the seasonal-mean vertically integrated water vapor transport vector from the NCEP reanalysis. Our study uses the hybrid model- observation P described to the left, E from the NCEP/DOE reanalysis (6-h, ), and F as the magnitude of the vertically integrated vapor transport in the NCEP/DOE reanalysis. Recycling ratios are calculated on a monthly basis, then averaged to seasonal values.  = a A b Recycling Ratio  for Area (km 2 ) Regionab Mackenzie %13.4%35.6% Khatanga %13.0%35.2% E. Europe %9.8%27.6% LaPlata %9.4%25.7% Kalahari %8.6%28.9% Ob %8.2%23.7% China %7.2%19.7% Talimakan %6.7%17.4% Congo %6.4%19.4% Mississippi %6.2%18.0% Australia %4.4%14.7% Amazon %3.7%10.8% Persia %3.0%8.6% W. Sahara %2.9%8.6% Mean (a,b) %8.4%24.3% Std (a,b) COV (a,b) Mean RR %8.5%24.3% Precipitation Several precipitation data sets are combined to produce a best estimate of precipitation sinks for the back- trajectory calculation. A hybrid 3-hourly precipitation data sets is produced from: NCEP-DOE reanalysis precipitation (6-hour forecast), interpolated to a 3-houly amount. Satellite-based CMORPH precipitation estimates (Joyce et al. 2004) to correct the diurnal cycle of reanalysis precipitation at low latitudes (see figure at right). 3-hourly CMORPH data are scaled from 0.25° to the reanalysis grid using bilinear interpolation. A centered 31-day running mean is calculated for each 3-hour interval of the CMORPH data to establish the mean diurnal cycle of precipitation and its variation throughout the year. All estimates scaled to agree with pentad totals from the observation-based CMAP data set (Xie and Arkin1997) on reanalysis grid. Low Latitudes: Mid & High Latitudes: One of the principal yardsticks for quantifying the strength of the hydrologic cycle over specific terrestrial regions is the recycling ratio. Definitions can vary, but commonly it is taken to be the fraction of precipitation over a defined area that originated as evapotranspiration from that same area, with no intervening cycles of precipitation or surface evapotranspiration. In the simplest sense one imagines that a change to evaporation over the area of concern has a direct and potentially predictable impact on local precipitation. Many methods have been used in the past – each has its shortcomings. The table on the right summarizes these. Quasi-Isentropic Back Trajectory (QIBT) Method We use a Lagrangian approach to determine the evaporative source regions for precipitation, assuming that between evaporation and precipitation, water vapor behaves as a passive tracer. Compared to the scale of the global gridded data, diabatic processes are local and only occur at either end of the trajectory. One exception is when a parcel is traced back into the ground – PBL heating is assumed and the parcel potential temperature is adjusted. We calculated bulk recycling values with length scales of 500 and 1000 km, for comparison to Trenberth (1999). The results were quite similar. Therefore, any differences between the two studies’ recycling maps are due to the method. A square region with area 10 5 km 2 has a side length of 316 km; a circular region has diameter 357 km. We selected a length scale of 340 km to compute bulk estimates for comparison to the QIBT recycling estimates. The bulk recycling results and differences of QIBT method from the bulk method are shown above. Recycling for basins in the GEWEX CSAs (left) during key seasons, and the relative changes in sources during the wettest and driest years. Note that units are not mass, but percent of total rain contributed by evaporation from each grid box. Recycling ratios for each basin are not rescaled to a common area.