Panel: Bill Large, Bob Weller, Tim Liu, Huug Van den Dool, Glenn White Surface fluxes Panel: Bill Large, Bob Weller, Tim Liu, Huug Van den Dool, Glenn White WG: Yochanan Kushnir, John Young, Ben Giese, Tsengdar Lee, David Legler Background: Fluxes between the atmosphere and the ocean, land surface, sea-ice are important for understanding climate variability - exchanges between the fast component and the memory in the climate system Surface forcing is a major source of error and uncertainty for ocean and land surface products Need better products and information on error statistics as input to ocean and land surface data assimilation Biases in NWP products/analyses have a deleterious effect on ocean and land simulations. These groups have generated their own products, correcting the fields input to flux computations. CLIVAR: Climate observation programs and process studies are helping to address the need for more accurate fluxes : Reference stations, ship-based observations, Process studies Space/time characteristics of fields and of error in fields, biases Parameterizations Withheld data for validation, model improvement Ocean observations provide an oceanographic constraint on fluxes

Findings (1) : Current Reanalysis surface flux products are not adequate for climate analyses (not accurate, budgets don’t close) or to force ocean and land surface models (not accurate). To date we note that there are significant biases in many locations. Assumption is often that problems with variability are not as severe as biases, but not necessarily justified - errors in bias and variability could be due to the same mechanism. Of particular note: significant biases in precipitation and radiation. Accurate precipitation is crucial for LSMs because of the positive feedback when coupled to the atmosphere. SW and LW errors are usually compensating, so it is important to use these from a common source (or address the bias in separate components). We need accurate surface fields more than accurate fluxes so that we can calculate our own surface fluxes. We replace reanalysis fields with corrected fields or other observational analyses (such as satellite-based surface radiation) when needed. Corrections for humidity are problematic.

Findings (2): Surface data are not being used in analyses in an optimal fashion; large differences between the state of the model and the in-situ data often lead to the exclusion of the data rather than to corrections to the analysis Since the analyses use prescribed SST, use of surface observations have not been a priority. Errors in SST have not been taken into account. Different (NCEP2, ERA15) NWP analyses are converging, but not to observations. Progress towards improved analyses should be gauged by comparisons with in situ data (e.g., SURFA). Sea-ice distribution is crucial: ice-ocean flux is much smaller than air-sea flux. Sea-ice fractional coverage is important in atmospheric analyses. Data-only products (e.g., precipitation, cloudiness, surface radiation) also have large uncertainties.

Analysis should be best estimate of the state - that’s what we measure Recommendations (1): Atmosphere, Ocean, Land Surface, Sea-ice analyses and the fluxes for each should be “synchronized” - coordinated programmatically Atmospheric analyses for climate purposes (such as CDAS) should be kept current Analysis should be best estimate of the state - that’s what we measure We should ensure that the fractional coverage of ocean, land, and sea-ice be represented within the atmospheric grid-box. Satellite estimates of sea-ice fraction are available for the modern era and should be used. Surface analyses should encompass: surface components (incl. spectral fluxes) and all the elements that feed into the calculations (10m wind components, wind speed, Tair, Qair, SST, atmospheric stability, cloud properties, SLP, sea state) stand-alone surface-only analyses that are adequate for forcing OGCMs and LSMs and for validating the surface fluxes from full atmospheric analyses and from coupled models surface analyses consistent with atmospheric analysis, assimilating available surface observations, but not feeding back to atmosphere realistic variability in the modern era down to 1 degree resolution globally, resolving diurnal cycle; regional resolution should be as high as feasible

Recommendations (2): Priorities: keep climate analysis current ensure fractional coverage of ocean, land, sea-ice within each atmospheric grid-box and produce fluxes for multiple surface types improve assimilation methods so that use of surface observations is optimized assimilate cloud and precipitation data include uncertainty in SST identify pilot period for continual testing of enhancements to models, parameterizations, assimilation methods, using withheld data for validation - 2 year period with good buoy/ship coverage and land coverage gauge progress towards improved analyses by comparisons with in situ data (e.g., SURFA).

Recommendations (3): R&D priorities: Improve cloud & PBL (atmosphere and ocean) representations so that analyses can produce realistic fluxes. Transition results from research (process studies, CPTs) Develop assimilation for coupled systems Improve assimilation methods so as to use surface observations more effectively Improve assimilation methods to use satellite observations (clouds, precipitation, radiation, moisture, etc) more effectively Other Pursue international collaboration on continual enhancement of observations (corrections, archeology)

R. Weller/WHOI

R. Weller/WHOI

Correcting Reanalyses (Large & Nurser, 2001) Corrected Air-Sea Heat Fluxes Wind speed correction factor (QSCAT based) Relative humidity correction (SOC and TAO based) W. Large/NCAR

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