Surface fluxes Panel: Bill Large, Bob Weller, Tim Liu, Huug Van den Dool, Glenn White Fluxes between the atmosphere and the ocean, land surface, sea-ice: Important for understanding climate variability - exchanges between the fast component and the memory in the climate system Surface forcing is the major source of error and uncertainty for ocean and land surface products Need better products and information on error statistics Reanalysis products (from 4DDA) or from “stand-alone” analyses?

Air-sea fluxes (Glenn White) The first reanalyses: Capable of producing reasonable surface stresses and latent and sensible heat Had major biases in solar radiation and cloudiness. In some cases net heat flux estimates from the reanalyses were improved by substituting satellite-derived surface net SW for the reanalysis field. Major uncertainties in air-sea fluxes remain Differences remain between satellite-based estimates of surface solar radiation and in-situ measurements. The magnitude of surface LW radiation more uncertain than surface SW. The magnitude of the hydrological cycle remains uncertain. The magnitude of the global mean climatological precipitation (and of evaporation as well) remains uncertain by more than 10%.

Air-sea fluxes, ctd (Glenn White) Global reanalyses with 4DDA: Best opportunity to get the most accurate sensible and latent heat fluxes and surface stresses Assimilate all observations and use the laws of physics to interpolate the observations in space and time - a good diagnostic of model’s parameterization of boundary layer, radiation and cloudiness Realistic model physics needed to correctly assimilate satellite fields related to precipitation and moisture Surface flux estimates from “stand-alone” analyses: very valuable (superior?) a check on the performance of global 4DDA reanalyses

Conclusions from ISSCP Comparisons ISSCP finds fewer 10% high and all clouds * ISSCP shows gradual decrease in all clouds ISSCP Tropical Clouds HIRS Tropical Clouds Courtesy: M. King/NASA,GSFC

Cloud Properties, July 2001 Pinker, Wang, King & Platnick GEWEX Newsletter ISCCPMODIS (TERRA) Cloud fraction Cloud optical depth Monthly mean SW surface downward flux ISCCPMODIS (TERRA) Sept 2001 Aug 2001 Jul 2001

October 1996October 1989 Average monthly snow depth derived from SSM/I T. Markus NASA/GSFC Distribution of snow thickness on sea ice is vital to understanding the overall heat exchange in the polar oceans. Thermal conductivity of snow is about an order of magnitude less than that of sea ice  a light snow cover on sea ice can greatly affect the heat flux between the surface and atmosphere. Snow depth on sea ice as a proxy for snowfall? Sea ice data assimilation Ingestion of satellite-derived sea ice concentrations to improve the representation of Antarctic polynyas - areas of intense air- sea heat fluxes (up to two orders of magnitude greater than the heat flux through the sea ice) and thus of intense ice production and brine release. ARCTIC ANTARCTIC C. Parkinson NASA/GSFC Satellite Passive Microwave Sea-ice Record

Surface fluxes Incl. Contributions from: Paul Houser, Adam Schlosser, Matt Rodell, S.K. Yang Priorities for next Reanalysis (Glenn White) : Improving cloudiness (esp. low-level oceanic stratus clouds ) Testing the importance of the diurnal cycle in sea surface temperature. The assimilation of surface and near surface observations from island stations and the assimilation of ship observations. It is not clear that current data assimilation gives priority to the assimilation of surface data and the accuracy of its surface fields. Redesign data assimilation system to emphasize the assimilation of surface data and the generation of surface and near-surface fields? Future reanalyses should include: Snow, soil moisture, land skin temperature, vegetation sea-ice distribution SST runoff? Clear sky fluxes for assessing forcing. R-1 and ERAs have these archived. Not R-2 or GDAS. Spectral fluxes: PAR and UV-B on the surface. Surface fields every 3 hours

Surface fluxes Incl. Contributions from: Paul Houser, Adam Schlosser, Matt Rodell, S.K. Yang, G. White How to “validate”?: Collaboration with SEAFLUX (satellite obs: Curry & Rossow) and SURFA (in situ obs: Gleckler) & efforts to evaluate flux parameterizations Sensitivity experiments as in GSWP2: global, multi-model simulations of land storage and fluxes PILPS: continued multi-model, international effort at evaluating the local-scale, land-model processes Simulated water storage memory (i.e. soil moisture and snow) - one of the controls of water storage on surface fluxes is an "inherent" control of land memory Global Soil Moisture Data Bank (led by Alan Robock)

Similar patterns, different magnitude. Note funny structure over Hawaii in CDAS; illustrates problem with island stations. NCEP1 reanalysis minus SRB1 estimate of net short wave at surface. Large differences. Similar differences in other reanalyses.

lower bound on uncertainty in our knowledge of precip. CMAP GPCP - CMAP

CDAS GDAS (5/00- 4/01) GDAS (10/01- 9/02) K &TRangeSRB P (mm/d) E P-E SH (W/m 2 ) LH SFC DSW USW NSW DLW ULW NLW NET RAD NHF Note wide range of estimates considered by Kiehl & Trenberth - an indication of uncertainty in our knowledge of surface fluxes.