THE GLOBAL ATMOSPHERIC THE GLOBAL ATMOSPHERIC HYDROLOGICAL CYCLE: Past, HYDROLOGICAL CYCLE: Past,

Present and FuturePresent and Future(What do we really know and how do we know (What do we really know and how do we know

it?)it?)

Phil Arkin, Cooperative Institute for Climate Phil Arkin, Cooperative Institute for Climate StudiesStudies

Earth System Science Interdisciplinary Center, Earth System Science Interdisciplinary Center, University of MarylandUniversity of Maryland

Research ResultsResearch Results Climate models indicate that global temperature Climate models indicate that global temperature

increases will be accompanied by changes in water increases will be accompanied by changes in water vapor and precipitation:vapor and precipitation: Water vapor increases to maintain roughly constant relative Water vapor increases to maintain roughly constant relative

humidity (about 7% per degree)humidity (about 7% per degree) Precipitation increases but at a slower rate (about 2-3% per Precipitation increases but at a slower rate (about 2-3% per

degree) degree) Regionally, precipitation intensifies in climatologically favored Regionally, precipitation intensifies in climatologically favored

regions, decreases at margins (“rich get richer”)regions, decreases at margins (“rich get richer”) Observations show:Observations show:

Global water vapor has increased recently as temperatures Global water vapor has increased recently as temperatures have warmed (but data have limitations)have warmed (but data have limitations)

Global precipitation has increases at 7%/degree since 1990 Global precipitation has increases at 7%/degree since 1990 (Wentz et al., 2007) or at 2.3%/degree (Adler et al., 2008), but (Wentz et al., 2007) or at 2.3%/degree (Adler et al., 2008), but again the data have shortcomingsagain the data have shortcomings

Rain gauge observations show increases in intense Rain gauge observations show increases in intense precipitation, but current datasets aren’t adequate to test the precipitation, but current datasets aren’t adequate to test the rich get richer hypothesisrich get richer hypothesis

Here I will discuss the origins and shortcomings of the Here I will discuss the origins and shortcomings of the datasets that are used to describe the atmospheric datasets that are used to describe the atmospheric hydrological cycle, and try to summarize the current hydrological cycle, and try to summarize the current ability of observations to test modelsability of observations to test models

What is the hydrological cycle?What is the hydrological cycle?(depends on what you’re talking about, of course)(depends on what you’re talking about, of course)

For the Earth, For the Earth, it’s the it’s the reservoirs of reservoirs of water and the water and the transfers transfers among themamong them

It matters to It matters to the climate the climate because of because of water’s ability water’s ability to transfer heat to transfer heat in a latent in a latent statestate

It matters to It matters to people because people because precipitation is precipitation is the original the original source of source of almost all fresh almost all fresh water we usewater we use(From UCAR web site)

Vertically integrated water Vertically integrated water balance equation for the balance equation for the

atmosphereatmosphere

- 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

Observing the components of the Observing the components of the atmospheric hydrological cycleatmospheric hydrological cycle

The surface exchanges and atmospheric water The surface exchanges and atmospheric water vapor amounts are crucialvapor amounts are crucial

Precipitation: “measured” by various methods; Precipitation: “measured” by various methods; global datasets existglobal datasets exist

Evaporation: estimated from turbulent flux Evaporation: estimated from turbulent flux theory and associated measureable parameters; theory and associated measureable parameters; oceanic datasets exist oceanic datasets exist

Atmospheric water vapor: measured by Atmospheric water vapor: measured by radiosondes, but with significant errors and poor radiosondes, but with significant errors and poor sampling; estimated over oceans from satellite sampling; estimated over oceans from satellite observations; limited global datasets existobservations; limited global datasets exist

Atmospheric transports: estimated by Atmospheric transports: estimated by atmospheric general circulation models from atmospheric general circulation models from observations/predictions of humidity and winds; observations/predictions of humidity and winds; global datasets existglobal datasets exist

Scales, Domains, Processes that Global Scales, Domains, Processes that Global Datasets Could/Should AddressDatasets Could/Should Address

Global: Global: Averaged over the globe, transports drop outAveraged over the globe, transports drop out Role of changes in storage depends on time scale; Role of changes in storage depends on time scale;

roughly speaking, P = Eroughly speaking, P = E How do water vapor (W, PHow do water vapor (W, PWATWAT) and P change as T) and P change as TSS

increases?increases? Land/Ocean; NH/SH:Land/Ocean; NH/SH:

Global monsoon scale, ENSOGlobal monsoon scale, ENSO Complementary variations in P, W over land not as tied to Complementary variations in P, W over land not as tied to TTS S

as over oceanas over ocean Can models simulate the observed annual cycle, interannual Can models simulate the observed annual cycle, interannual

variability?variability? Continental:Continental:

Large river basins, major mountain rangesLarge river basins, major mountain ranges Synoptic systems, diurnal convectionSynoptic systems, diurnal convection

Local:Local: Smaller basins on downSmaller basins on down Not the target of global datasets (so far)Not the target of global datasets (so far)

Creating Global DatasetsCreating Global Datasets Three main methods: Observations, theory and Three main methods: Observations, theory and

combined combined Observation-based:Observation-based:

Direct measurements only possible for some parameters in a Direct measurements only possible for some parameters in a few spots – rain gauges, radiosondesfew spots – rain gauges, radiosondes

Remote sensing used to infer (not measure) precipitation, Remote sensing used to infer (not measure) precipitation, winds, temperatures, moisture – radars/profilers, satellite winds, temperatures, moisture – radars/profilers, satellite instrumentsinstruments

Some parameters, like oceanic evaporation, can’t be directly Some parameters, like oceanic evaporation, can’t be directly measured at all measured at all

Theoretically-based:Theoretically-based: Fluid dynamics permit simulation of atmospheric properties Fluid dynamics permit simulation of atmospheric properties

in general circulation modelsin general circulation models Augmentation with parameterizations based on combination Augmentation with parameterizations based on combination

of theory and empiricism enables simulation of evaporation, of theory and empiricism enables simulation of evaporation, clouds, precipitationclouds, precipitation

Combinations:Combinations: Models can be used to combine observations of various sorts Models can be used to combine observations of various sorts

with theory to derive globally complete datasetswith theory to derive globally complete datasets Data assimilation common used as label for this processData assimilation common used as label for this process

Observing PrecipitationObserving Precipitation Not uniformly well defined – generally speaking we Not uniformly well defined – generally speaking we

attempt to obtain spatial and/or temporal means, but attempt to obtain spatial and/or temporal means, but rigorous definitions are not typicalrigorous definitions are not typical

Gauges – point values with relatively well understood Gauges – point values with relatively well understood errorserrors

Remote Sensing – radars (surface and space), space-Remote Sensing – radars (surface and space), space-based infrared and microwave radiometers based infrared and microwave radiometers All are inferencesAll are inferences Errors vary in time and space and are poorly Errors vary in time and space and are poorly

known/understoodknown/understood ModelsModels

Observed/estimated winds, temperature, moisture Observed/estimated winds, temperature, moisture provide information on where precipitation will occur in provide information on where precipitation will occur in near futurenear future

This is done regularly for weather forecasts; can be used This is done regularly for weather forecasts; can be used in areas where other information is limitedin areas where other information is limited

Using such forecasts in global datasets moves them into Using such forecasts in global datasets moves them into the “combined” categorythe “combined” category

Integrating/Analyzing Precipitation Integrating/Analyzing Precipitation EstimatesEstimates

Satellite-derived estimates have complementary Satellite-derived estimates have complementary characteristics (geostationary infrared is more characteristics (geostationary infrared is more complete but has poor accuracy, low Earth orbit complete but has poor accuracy, low Earth orbit passive microwave is more accurate but has sparse passive microwave is more accurate but has sparse sampling) sampling)

Satellite-derived estimates have biases that can be Satellite-derived estimates have biases that can be reduced/removed by adding information from rain reduced/removed by adding information from rain gaugesgauges

Since the input data are not uniformly distributed in Since the input data are not uniformly distributed in time and space, an analysis (method for creating time and space, an analysis (method for creating complete in time and space fields from varying and complete in time and space fields from varying and incomplete observations) must be used to create the incomplete observations) must be used to create the final datasetfinal dataset

Analysis can be statistical combination of inputs, or Analysis can be statistical combination of inputs, or simply a composite, or include an atmospheric model simply a composite, or include an atmospheric model (combined observation and theory)(combined observation and theory)

Global Precipitation DatasetsGlobal 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

Multi-Source Analysis of Precipitation Multi-Source Analysis of Precipitation (MSAP)(MSAP)

Used OI to produce Used OI to produce blend of ERA-40 (now blend of ERA-40 (now includes ERA-I) and includes ERA-I) and SSM/I (GPROF & Wentz)SSM/I (GPROF & Wentz)

Relies on satellite Relies on satellite estimates in tropics, estimates in tropics, reanalysis in high reanalysis in high latitudes, mix in latitudes, mix in betweenbetween

Example of combined Example of combined approach (not data approach (not data assimilation, though)assimilation, though)

Theoretical (Model-Based) PrecipitationTheoretical (Model-Based) Precipitation

The Intergovernmental Panel on Climate The Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC Change Fourth Assessment Report (IPCC AR4) was based on a large number of model AR4) was based on a large number of model simulations of future climatesimulations of future climate

Many of these models were used to simulate Many of these models were used to simulate the 20the 20thth Century and precipitation from those Century and precipitation from those runs represents theoretical calculations of runs represents theoretical calculations of global precipitationglobal precipitation

Those results can be compared to global Those results can be compared to global precipitation datasetsprecipitation datasets

+/- 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 Datasets based on observations are in lower part of

AR4 rangeAR4 range

Note scale changed by factor of 10Note scale changed by factor of 10 Biases removed so means are the same for all time seriesBiases removed so means are the same for all time series AR4 ensemble mean exhibits much less variability since it AR4 ensemble mean exhibits much less variability since it

is an average of many (20 or so) runsis an average of many (20 or so) runs

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

CCA and AR4 ensemble mean show similar centennial-scale CCA and AR4 ensemble mean show similar centennial-scale changes, but interannual variations rather differentchanges, but interannual variations rather different

Data assimilation-based precipitation has realistic looking Data assimilation-based precipitation has realistic looking

variability on fine scales – what about global means?variability on fine scales – what about global means?

TMPA 3-Hrly CMORPH 3-Hrly

MERRA 3-Hrly MERRA 3-Hrly

First 7 days of January 2004

Datasets based on observations (GPCP, CMAP) give about 2.6 Datasets based on observations (GPCP, CMAP) give about 2.6 mm/day (AR4 range is about 2.5-3.2 mm/day)mm/day (AR4 range is about 2.5-3.2 mm/day)

Data assimilation products average about 3 mm/day; also have Data assimilation products average about 3 mm/day; also have larger mean annual cycle and greater interannual variabilitylarger mean annual cycle and greater interannual variability

Global Mean Precipitation from Data Global Mean Precipitation from Data AssimilationAssimilation

EvaporationEvaporation No actual observations of evaporation exist – not really an No actual observations of evaporation exist – not really an

observable quantityobservable quantity Relatively simple models based on parameterizations of Relatively simple models based on parameterizations of

turbulent fluxes can be used to calculate oceanic evaporationturbulent fluxes can be used to calculate oceanic evaporation Require wind speed, near-surface gradient in temperature/humidityRequire wind speed, near-surface gradient in temperature/humidity Satellite-derived estimates of SST and wind speed are available and can Satellite-derived estimates of SST and wind speed are available and can

be usedbe used Numerous datasets exist (Tim Liu of JPL was first person I Numerous datasets exist (Tim Liu of JPL was first person I

heard talk about this – not sure why he isn’t on this list): 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; Goddard Satellite-Based Surface Turbulent Fluxes Version 2 (GSSTF2;

Chou et al. 2003)Chou et al. 2003) Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data

Version 3 (HOAPS3; Grassl et al. 2000)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) Observation-based land evaporation (evapotranspiration) datasets do not exist so far as I knowdatasets do not exist so far as I know

Both theoretical and data assimilation global evaporation Both theoretical and data assimilation global evaporation datasets exist, but confidence in their details is lowdatasets exist, but confidence in their details is low

Atmospheric Water Vapor/ConvergenceAtmospheric Water Vapor/Convergence Radiosonde observations include relative humidity; combined Radiosonde observations include relative humidity; combined

with temperature can be used to calculate specific with temperature can be used to calculate specific humidity/water vaporhumidity/water vapor Poor samplingPoor sampling Significant instrumental errorsSignificant instrumental errors

Satellite observations can be used to estimate total column water Satellite observations can be used to estimate total column water vapor and its vertical profilevapor 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 only1988 – 1999 only

Calculating convergence/divergence from observed winds alone Calculating convergence/divergence from observed winds alone is not possible; models are requiredis not possible; models are required Fortunately, data assimilation wind fields are adequate for this purposeFortunately, data assimilation wind fields are adequate for this purpose Unfortunately, data assimilation-based water vapor products are not Unfortunately, data assimilation-based water vapor products are not

viewed as positively; however, global water vapor and water vapor flux viewed as positively; however, global water vapor and water vapor flux datasets from reanalysis are widely useddatasets from reanalysis are widely used

What aspects of the hydrological cycle can we What aspects of the hydrological cycle can we test these datasets on?test these datasets on?

Global climate models project large increases in global Global climate models project large increases in global mean temperature, accompanied with increases in mean temperature, accompanied with increases in water vapor and precipitationwater vapor and precipitation Can available global datasets help support these model Can available global datasets help support these model

findings?findings? Mean annual cycle of global temperature is substantialMean annual cycle of global temperature is substantial

Is it associated with changes in water vapor and Is it associated with changes in water vapor and precipitation?precipitation?

Interannual variability: the El NiInterannual variability: the El Niño/Southern ño/Southern Oscillation is associated with increased tropospheric Oscillation is associated with increased tropospheric temperature globallytemperature globally What about global water vapor/precipitation?What about global water vapor/precipitation?

Mean annual cycle: T, P, E, WV from data Mean annual cycle: T, P, E, WV from data assimilationassimilation

Mean annual cycle: Temperature and Precipitation Mean annual cycle: Temperature and Precipitation from Observationsfrom Observations

Difference between CMAP and GPCP due to differences Difference between CMAP and GPCP due to differences over the ocean – no independent validation availableover the ocean – no independent validation available

Ocean temperature and reanalysis atmospheric water vapor

Temperature (red in top panel) and Water Vapor

Conclusions/Issues (distressingly incomplete)Conclusions/Issues (distressingly incomplete) Global data sets needed to describe the global Global data sets needed to describe the global

hydrological cycle require some combined hydrological cycle require some combined (theory/model + observation) input(theory/model + observation) input Water vapor probably best, precipitation needs Water vapor probably best, precipitation needs

improvementimprovement Evaporation dependent on model accuracyEvaporation dependent on model accuracy

Variability in precipitation data sets, even for Variability in precipitation data sets, even for whole 20whole 20thth Century, looks reasonable Century, looks reasonable

Water vapor short-term variations look good; Water vapor short-term variations look good; not as good on longer time scalesnot as good on longer time scales

Evaporation (not shown here) hard to evaluate Evaporation (not shown here) hard to evaluate due to dependence on models and other due to dependence on models and other observations like surface windsobservations like surface winds