1. THE GLOBAL ATMOSPHERIC HYDROLOGICAL CYCLE: Past, Present and Future (What do we really know and how do we know it?) Phil Arkin, Cooperative Institute for Climate Studies 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 increases 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 increases 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, and try to summarize the current ability of observations to test models Here I will discuss the origins and shortcomings of the datasets that are used to describe the atmospheric hydrological cycle, and try to summarize the current ability of observations to test models

3. Vertically integrated water balance equation for the atmosphere - liquid and solid water small compared to vapor – neglected here - balance is between changes in storage (vertically integrated specific humidity or precipitable water) and horizontal convergence, evaporation and precipitation

4. 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; oceanic datasets exist Evaporation: estimated from turbulent flux theory and associated measureable parameters; oceanic 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 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 Atmospheric transports: estimated by atmospheric general circulation models from observations/predictions of humidity and winds; global datasets exist

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 Direct measurements only possible for some parameters in a few spots – rain gauges, radiosondes 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, can’t be directly measured at all Some parameters, like oceanic evaporation, can’t be 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 common used as label for this process Data assimilation common used as label for this process

6. Observing Precipitation Not uniformly well defined – generally speaking we attempt to obtain spatial and/or temporal means, but rigorous definitions are not typical Not uniformly well defined – generally speaking we attempt to obtain spatial and/or temporal means, but rigorous definitions are not typical Gauges – point values with relatively well understood errors Gauges – point values with relatively well understood errors 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 them into the “combined” category Using such forecasts in global datasets moves them into the “combined” category

7. Theoretical (Model-Based) 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

8. +/- 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 Datasets based on observations are in lower part of AR4 range

9. Note scale changed by factor of 10 Note scale changed by factor of 10 Biases removed so means are the same for all time series Biases removed so means are the same for all time series AR4 ensemble mean exhibits much less variability since it is an average of many (20 or so) runs AR4 ensemble mean exhibits much less variability since it is an average of many (20 or so) runs

10. Re-scale AR4 ensemble mean so variance is about same as a single realization Re-scale AR4 ensemble mean 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

11. Data assimilation-based precipitation has realistic looking variability on fine scales – what about global means? TMPA 3-HrlyCMORPH 3-Hrly MERRA 3-Hrly First 7 days of January 2004

12. 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

13. Evaporation No actual observations of evaporation exist – not really an observable quantity No actual observations of evaporation exist – not really an observable quantity 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 (Tim Liu of JPL was first person I heard talk about this – not sure why he isn’t on this list): Numerous datasets exist (Tim Liu of JPL was first person I heard talk about this – not sure why he isn’t on this list): 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 Observation-based land evaporation (evapotranspiration) datasets do not exist so far as I know Both theoretical and data assimilation global evaporation datasets exist, but confidence in their details is low Both theoretical and data assimilation global evaporation datasets exist, but confidence in their details is low

14. 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 (others may/should be in development): One dataset exists (others may/should be in development): NVAP (Randel and Vonder Haar, CSU) NVAP (Randel and Vonder Haar, CSU) 1988 – 1999 only 1988 – 1999 only 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

15. What aspects of the hydrological cycle can we test these datasets on? Global climate models project large increases in global mean temperature, accompanied with increases in water vapor and precipitation Global climate models project large increases in global mean temperature, accompanied with increases in water vapor and precipitation Can available global datasets help support these model findings? Can available global datasets help support these model findings? Mean annual cycle of global temperature is substantial Mean annual cycle of global temperature is substantial Is it associated with changes in water vapor and precipitation? Is it associated with changes in water vapor and precipitation? Interannual variability: the El Niño/Southern Oscillation is associated with increased tropospheric temperature globally Interannual variability: the El Niño/Southern Oscillation is associated with increased tropospheric temperature globally What about global water vapor/precipitation? What about global water vapor/precipitation?

16. Mean annual cycle: T, P, E, WV from data assimilation

17. Mean annual cycle: Temperature and Precipitation from Observations Difference between CMAP and GPCP due to differences over the ocean – no independent validation available

18. Ocean temperature and reanalysis atmospheric water vapor

19. Temperature (red in top panel) and Water Vapor

20. Conclusions/Issues (distressingly incomplete) Global data sets needed to describe the global hydrological cycle require some combined (theory/model + observation) input Global data sets needed to describe the global hydrological cycle require some combined (theory/model + observation) input Water vapor probably best, precipitation needs improvement Water vapor probably best, precipitation needs improvement Evaporation dependent on model accuracy Evaporation dependent on model accuracy Variability in precipitation data sets, even for whole 20 th Century, looks reasonable Variability in precipitation data sets, even for whole 20 th Century, looks reasonable Water vapor short-term variations look good; not as good on longer time scales Water vapor short-term variations look good; not as good on longer time scales Evaporation (not shown here) hard to evaluate due to dependence on models and other observations like surface winds Evaporation (not shown here) hard to evaluate due to dependence on models and other observations like surface winds