1. OBSERVING THE GLOBAL HYDROLOGICAL CYCLE: What we think we know and why Phil Arkin, Cooperative Institute for Climate and Satellites Earth System Science Interdisciplinary Center, University of Maryland

2. Research Results Climate models indicate that global temperature increases will be accompanied by changes in water vapor and precipitation: Climate models indicate that global temperature increases will be accompanied by changes in water vapor and precipitation: Water vapor increases to maintain roughly constant relative humidity (about 7% per degree) Water vapor increases to maintain roughly constant relative humidity (about 7% per degree) Precipitation increases but at a slower rate (about 2-3% per degree) Precipitation increases but at a slower rate (about 2-3% per degree) Regionally, precipitation intensifies in climatologically favored regions, decreases at margins (“rich get richer”) Regionally, precipitation intensifies in climatologically favored regions, decreases at margins (“rich get richer”) Observations show: Observations show: Global water vapor has increased recently as temperatures have warmed (but data have limitations) Global water vapor has increased recently as temperatures have warmed (but data have limitations) Global precipitation has increased at 7%/degree since 1990 (Wentz et al., 2007) or at 2.3%/degree (Adler et al., 2008), but again the data have shortcomings Global precipitation has increased at 7%/degree since 1990 (Wentz et al., 2007) or at 2.3%/degree (Adler et al., 2008), but again the data have shortcomings Rain gauge observations show increases in intense precipitation, but current datasets aren’t adequate to test the rich get richer hypothesis Rain gauge observations show increases in intense precipitation, but current datasets aren’t adequate to test the rich get richer hypothesis Here I will discuss the origins and shortcomings of the datasets that are used to describe the atmospheric hydrological cycle, focusing mainly on precipitation Here I will discuss the origins and shortcomings of the datasets that are used to describe the atmospheric hydrological cycle, focusing mainly on precipitation

3. What is the hydrological cycle? (depends on what you’re talking about, of course) For the Earth, it’s the reservoirs of water and the transfers among them For the Earth, it’s the reservoirs of water and the transfers among them It matters to the climate because of water’s ability to transfer heat in a latent state It matters to the climate because of water’s ability to transfer heat in a latent state It matters to people because precipitation is the original source of almost all fresh water we use It matters to people because precipitation is the original source of almost all fresh water we use (From UCAR web site)

4. Vertically integrated water balance equation for the atmosphere - Liquid and solid water small compared to vapor – neglected here - Balance is between changes in storage and horizontal convergence, evaporation and precipitation – what is the quality of those data sets?

5. Creating Global Datasets Three main methods: Observations, theory and combined Three main methods: Observations, theory and combined Observation-based: Observation-based: Direct measurements only possible for some parameters in a few spots – rain gauges, radiosondes, pan evaporation Direct measurements only possible for some parameters in a few spots – rain gauges, radiosondes, pan evaporation Remote sensing used to infer (not measure) precipitation, winds, temperatures, moisture – radars/profilers, satellite instruments Remote sensing used to infer (not measure) precipitation, winds, temperatures, moisture – radars/profilers, satellite instruments Some parameters, like oceanic evaporation, are not directly measured at all Some parameters, like oceanic evaporation, are not directly measured at all Theoretically-based: Theoretically-based: Fluid dynamics permit simulation of atmospheric properties in general circulation models Fluid dynamics permit simulation of atmospheric properties in general circulation models Augmentation with parameterizations based on combination of theory and empiricism enables simulation of evaporation, clouds, precipitation Augmentation with parameterizations based on combination of theory and empiricism enables simulation of evaporation, clouds, precipitation Combinations: Combinations: Models can be used to combine observations of various sorts with theory to derive globally complete datasets Models can be used to combine observations of various sorts with theory to derive globally complete datasets Data assimilation commonly used as label for this process Data assimilation commonly used as label for this process

6. Observing the components of the atmospheric hydrological cycle The surface exchanges and atmospheric water vapor amounts are crucial The surface exchanges and atmospheric water vapor amounts are crucial Precipitation: “measured” by various methods; global datasets exist Precipitation: “measured” by various methods; global datasets exist Evaporation: estimated from turbulent flux theory and associated measureable parameters; “observed” oceanic datasets exist; evapotranspiration over land can be estimated from observations except for the winds Evaporation: estimated from turbulent flux theory and associated measureable parameters; “observed” oceanic datasets exist; evapotranspiration over land can be estimated from observations except for the winds Atmospheric water vapor: measured by radiosondes, but with significant errors and poor sampling; estimated over oceans from satellite observations; limited global datasets exist Atmospheric water vapor: measured by radiosondes, but with significant errors and poor sampling; estimated over oceans from satellite observations; limited global datasets exist Atmospheric transports: estimated by atmospheric general circulation models from observations/predictions of humidity and winds; global datasets exist, but only with the use of models Atmospheric transports: estimated by atmospheric general circulation models from observations/predictions of humidity and winds; global datasets exist, but only with the use of models

7. Evaporation No actual direct observations of real evaporation exist – not really an observable quantity (pan evaporation is an estimate of the upper bound over land) No actual direct observations of real evaporation exist – not really an observable quantity (pan evaporation is an estimate of the upper bound over land) Relatively simple models based on parameterizations of turbulent fluxes can be used to calculate oceanic evaporation Relatively simple models based on parameterizations of turbulent fluxes can be used to calculate oceanic evaporation Require wind speed, near-surface gradient in temperature/humidity Require wind speed, near-surface gradient in temperature/humidity Satellite-derived estimates of SST and wind speed are available and can be used Satellite-derived estimates of SST and wind speed are available and can be used Numerous datasets exist – here are 4 reasonably current ones: Numerous datasets exist – here are 4 reasonably current ones: WHOI OAFlux (Yu and Weller, 2007) WHOI OAFlux (Yu and Weller, 2007) Goddard Satellite-Based Surface Turbulent Fluxes Version 2 (GSSTF2; Chou et al. 2003) Goddard Satellite-Based Surface Turbulent Fluxes Version 2 (GSSTF2; Chou et al. 2003) Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data Version 3 (HOAPS3; Grassl et al. 2000) Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data Version 3 (HOAPS3; Grassl et al. 2000) Remote Sensing Systems UMORA (Wentz et al. 2007) Remote Sensing Systems UMORA (Wentz et al. 2007) Observation-based land evaporation (evapotranspiration) datasets do not exist so far as I know – everything but winds can be derived from satellite data, but the winds are crucial Observation-based land evaporation (evapotranspiration) datasets do not exist so far as I know – everything but winds can be derived from satellite data, but the winds are crucial Data assimilation-based global evaporation datasets exist, but confidence in their details is low Data assimilation-based global evaporation datasets exist, but confidence in their details is low

8. Atmospheric Water Vapor/Convergence Radiosonde observations include relative humidity; combined with temperature can be used to calculate specific humidity/water vapor Radiosonde observations include relative humidity; combined with temperature can be used to calculate specific humidity/water vapor Poor sampling Poor sampling Significant instrumental errors Significant instrumental errors Satellite observations can be used to estimate total column water vapor and its vertical profile Satellite observations can be used to estimate total column water vapor and its vertical profile One dataset exists: One dataset exists: NVAP (Randel and Vonder Haar, CSU) NVAP (Randel and Vonder Haar, CSU) 1988 – 1999 only; currently being updated to extend the record and incorporate newer satellite data 1988 – 1999 only; currently being updated to extend the record and incorporate newer satellite data Calculating convergence/divergence from observed winds alone is not possible; models are required Calculating convergence/divergence from observed winds alone is not possible; models are required Fortunately, data assimilation wind fields are adequate for this purpose Fortunately, data assimilation wind fields are adequate for this purpose Unfortunately, data assimilation-based water vapor products are not viewed as positively; however, global water vapor and water vapor flux datasets from reanalysis are widely used Unfortunately, data assimilation-based water vapor products are not viewed as positively; however, global water vapor and water vapor flux datasets from reanalysis are widely used

9. Observing Precipitation Gauges – point values with relatively well understood errors (but with atrocious spatial sampling characteristics) Gauges – point values with relatively well understood errors (but with atrocious spatial sampling characteristics) Remote Sensing – radars (surface and space), space-based infrared and microwave radiometers Remote Sensing – radars (surface and space), space-based infrared and microwave radiometers All are inferences All are inferences Errors vary in time and space and are poorly known/understood Errors vary in time and space and are poorly known/understood Models Models Observed/estimated winds, temperature, moisture provide information on where precipitation will occur in near future Observed/estimated winds, temperature, moisture provide information on where precipitation will occur in near future This is done regularly for weather forecasts; can be used in areas where other information is limited This is done regularly for weather forecasts; can be used in areas where other information is limited Using such forecasts in global datasets moves the resulting products into the “combined” category Using such forecasts in global datasets moves the resulting products into the “combined” category

10. Estimating Precipitation from Satellite Observations Visible and/or infrared (IR) Visible and/or infrared (IR) Essentially an index of cloudiness, usually with embellishment Essentially an index of cloudiness, usually with embellishment Related to precipitation amount statistically, with various calibrations Related to precipitation amount statistically, with various calibrations Passive microwave - emission Passive microwave - emission Ocean only, coarse resolution, mediocre sampling Ocean only, coarse resolution, mediocre sampling Relatively directly related to liquid water amount Relatively directly related to liquid water amount Things like freezing level, cloud non-precipitating liquid have to be determined somehow Things like freezing level, cloud non-precipitating liquid have to be determined somehow No snow signal No snow signal Passive microwave - scattering Passive microwave - scattering Intermediate between IR and emission Intermediate between IR and emission Signal based on precipitation-size ice, so no warm rain signal Signal based on precipitation-size ice, so no warm rain signal Ice/snow on surface looks like heavy precipitation Ice/snow on surface looks like heavy precipitation Resolution better than emission, worse than IR Resolution better than emission, worse than IR Sampling similar to emission Sampling similar to emission

11. Integrating/Analyzing Precipitation Estimates Satellite-derived estimates have complementary characteristics (geostationary infrared is more complete but has poor accuracy, low Earth orbit passive microwave is more accurate but has sparse sampling) Satellite-derived estimates have complementary characteristics (geostationary infrared is more complete but has poor accuracy, low Earth orbit passive microwave is more accurate but has sparse sampling) Satellite-derived estimates have biases that can be reduced/removed by adding information from rain gauges Satellite-derived estimates have biases that can be reduced/removed by adding information from rain gauges Since the input data are not uniformly distributed in time and space, an analysis (method for creating complete in time and space fields from varying and incomplete observations) must be used to create the final dataset Since the input data are not uniformly distributed in time and space, an analysis (method for creating complete in time and space fields from varying and incomplete observations) must be used to create the final dataset Analysis can be statistical combination of inputs, or simply a composite, or include an atmospheric model (combined observation and theory) Analysis can be statistical combination of inputs, or simply a composite, or include an atmospheric model (combined observation and theory)

12. Global Precipitation Datasets GPCP (left)/CMAP (right) mean annual cycle and global mean time series Monthly/5-day; 2.5° lat/long global Both based on microwave/IR combined with gauges

13. o Using reanalysis-derived precipitation as an input is one way of combining theoretical and observed results to get global data o What about using the reanalysis (or NWP) results by themselves? Certainly look reasonable enough (above)…. TMPA 3-HrlyCMORPH 3-Hrly MERRA 3-Hrly First 7 days of January 2004

14. Multi-Source Analysis of Precipitation (MSAP) Used OI to produce blend of ERA-40 (now includes ERA-I) and SSM/I (GPROF & Wentz) Used OI to produce blend of ERA-40 (now includes ERA-I) and SSM/I (GPROF & Wentz) Relies on satellite estimates in tropics, reanalysis in high latitudes, mix in between Relies on satellite estimates in tropics, reanalysis in high latitudes, mix in between Example of combined approach Example of combined approach See Sapiano et al., 2008, JGR See Sapiano et al., 2008, JGR

15. Datasets based on observations (GPCP, CMAP) give about 2.6 mm/day (AR4 range is about 2.5-3.2 mm/day) Datasets based on observations (GPCP, CMAP) give about 2.6 mm/day (AR4 range is about 2.5-3.2 mm/day) Data assimilation products average about 3 mm/day; also have larger mean annual cycle and greater interannual variability Data assimilation products average about 3 mm/day; also have larger mean annual cycle and greater interannual variability Global Mean Precipitation from Data Assimilation Junye Chen, ESSIC and GMAO/MERRA

16. Model-Based (Theoretical) Precipitation The Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) was based on a large number of model simulations of future climate The Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) was based on a large number of model simulations of future climate Many of these models were used to simulate the 20 th Century and precipitation from those runs represents theoretical calculations of global precipitation Many of these models were used to simulate the 20 th Century and precipitation from those runs represents theoretical calculations of global precipitation Those results can be compared to global precipitation datasets Those results can be compared to global precipitation datasets

17. To evaluate these model results, we need longer time series of global precipitation analyses : To evaluate these model results, we need longer time series of global precipitation analyses : Approach: reconstruct/reanalyze global precipitation back to 1900 using 2 methods Approach: reconstruct/reanalyze global precipitation back to 1900 using 2 methods Empirical Orthogonal Function (EOF)-based reconstruction using GPCP and other global precipitation analyses, combined with historical coastal and island rain gauge observations Empirical Orthogonal Function (EOF)-based reconstruction using GPCP and other global precipitation analyses, combined with historical coastal and island rain gauge observations Canonical Correlation Analysis (CCA) reanalysis using SST and SLP, based on modern era analyses Canonical Correlation Analysis (CCA) reanalysis using SST and SLP, based on modern era analyses

18. CCA Reanalyses Anomalies relative to 1979 – 2007 base period Anomalies relative to 1979 – 2007 base period Decadal-scale signal looks plausible Decadal-scale signal looks plausible Ability to resolve finer scale phenomena like ENSO is limited due to coarse resolution (yearly, 5°x5°); bigger errors on short time scales (Smith et. al. 2009, JGR) Ability to resolve finer scale phenomena like ENSO is limited due to coarse resolution (yearly, 5°x5°); bigger errors on short time scales (Smith et. al. 2009, JGR) EOF-based reconstructions (not shown here) offer finer time/space resolution but fail to capture the decadal signal (Smith et. al. 2008, JGR) EOF-based reconstructions (not shown here) offer finer time/space resolution but fail to capture the decadal signal (Smith et. al. 2008, JGR) Fig 1: DJF means.

19. X X XXXXXXXXX X Southern Oscillation Index XXXXXXXXXX X ENSO Signal: Warm (top), Cold (Bottom); CCA (Left), EOF (Right) 1900 – 1998; Annual Anomalies

20. (mm/day units) CCA preserves ENSO signal well throughout 20 th Century EOF (based on MSAP, which is short base period) does not

21. Warm Phase Cool Phase Pacific Decadal Oscillation (PDO) From http://jisao.washington.edu/pdo (1930-1945) (1978-1998) (1950-1975)

22. CCA captures similarity between early and late warm periods, but is stronger amplitude real, or data-related artifact? EOF-MSAP loses detail in early period, but provides more spatial detail in later two periods

23. +/- 1 and 2 SD plotted for the ensemble of AR4 runs +/- 1 and 2 SD plotted for the ensemble of AR4 runs Datasets based on observations are in lower part of AR4 range, especially over oceans Datasets based on observations are in lower part of AR4 range, especially over oceans “Compo” is first version of NOAA/ESRL Historical Reanalysis (coordinated with ACRE) “Compo” is first version of NOAA/ESRL Historical Reanalysis (coordinated with ACRE)

24. Bias removed, and AR4 ensemble mean re-scaled so variance is about same as a single realization Bias removed, and AR4 ensemble mean re-scaled so variance is about same as a single realization CCA and AR4 ensemble mean show similar centennial-scale changes, but interannual variations rather different CCA and AR4 ensemble mean show similar centennial-scale changes, but interannual variations rather different Interannual variations in Compo reanalysis are more similar to CCA; trend is negative Interannual variations in Compo reanalysis are more similar to CCA; trend is negative Some of this is discussed in Smith et al., 2009, GRL Some of this is discussed in Smith et al., 2009, GRL

25. Conclusions/Issues Using data assimilation-derived precipitation offers the potential to improve global precipitation data sets, but… Using data assimilation-derived precipitation offers the potential to improve global precipitation data sets, but… All data assimilation products should be used with caution All data assimilation products should be used with caution Variability in precipitation data sets, even for whole 20 th Century, looks plausible, but the global means are significantly lower than the model-based values Variability in precipitation data sets, even for whole 20 th Century, looks plausible, but the global means are significantly lower than the model-based values Validation needed – how can we reconcile the model- observation differences? Validation needed – how can we reconcile the model- observation differences? Tropical oceanic rainfall calibrations are all based on limited samples Tropical oceanic rainfall calibrations are all based on limited samples None of the input data for the precipitation data sets is good over mountains or the polar caps None of the input data for the precipitation data sets is good over mountains or the polar caps Need some way to pin these values down better Need some way to pin these values down better Other components are never going to be unequivocally confirmed from observations alone – precipitation is the best observed, despite its problems Other components are never going to be unequivocally confirmed from observations alone – precipitation is the best observed, despite its problems